System and method for situational behavior of an autonomous vehicle

ABSTRACT

Systems and methods for situational behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes at least one perception sensor configured to generate perception data indicative of at least one other vehicle on a roadway, a non-transitory computer readable medium, and a processor. The processor is configured to determine that the other vehicle is violating one or more rules of the roadway based on the perception data, tag the other vehicle as a non-compliant driver, and modify control of the autonomous vehicle in response to tagging the other vehicle as a non-compliant driver.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 18/050,956, filed Oct. 28, 2022, which claims thebenefit of priority from U.S. Provisional Patent Application No.63/273,868, filed on Oct. 29, 2021. Any and all applications for which aforeign or domestic priority claim is identified in the Application DataSheet as filed with the present application are hereby incorporated byreference under 37 CFR 1.57.

BACKGROUND Technical Field

The present disclosure relates generally to autonomous vehicles. Moreparticularly, the present disclosure is related to operating anautonomous vehicle (AV) appropriately on public roads, highways, andlocations with other vehicles or pedestrians.

Description of the Related Technology

One aim of autonomous vehicle technologies is to provide vehicles thatcan safely navigate towards a destination with limited or no driverassistance. The safe navigation of an autonomous vehicle (AV) from onepoint to another may include the ability to signal other vehicles,navigating around other vehicles in shoulders or emergency lanes,changing lanes, biasing appropriately in a lane, and navigate allportions or types of highway lanes. Autonomous vehicle technologies mayenable an AV to operate without requiring extensive learning or trainingby surrounding drivers, by ensuring that the AV can operate safely, in away that is evident, logical, or familiar to surrounding drivers andpedestrians.

SUMMARY OF SOME INVENTIVE ASPECTS

Systems and methods are described herein that allow an autonomousvehicle (AV) to navigate from a first point to a second point without ahuman driver present in the AV and to comply with instructions for safeand lawful operation.

In one aspect, there is provided an autonomous vehicle configured todrive on a roadway, comprising: at least one perception sensorconfigured to generate perception data indicative of at least one othervehicle on the roadway; a processor; and a non-transitory computerreadable medium having stored thereon instructions that, when executedby the processor, cause the processor to: determine that the othervehicle is violating one or more rules of the roadway based on theperception data, tag the other vehicle as a non-compliant driver, andmodify control of the autonomous vehicle in response to tagging theother vehicle as a non-compliant driver.

In some embodiments, the processor is further configured to: determinethat the other vehicle is in a lane adjacent to that of the autonomousvehicle; determine that a portion of the other vehicle has crossed alane boundary on a side closest to the autonomous vehicle; in responseto determining that the other vehicle is in the lane adjacent to that ofthe autonomous vehicle and that the portion of the other vehicle hascrossed the lane boundary on the side closest to the autonomous vehicle,tag the other vehicle as a lane crossing non-compliant driver; andmonitor the lane crossing non-compliant driver for a potential cut intothe autonomous vehicle's lane.

In some embodiments, the processor is further configured to: determinethat the other vehicle is an erratic non-compliant driver in response todetermining that the other vehicle has broken traffic laws in one ormore of the following ways: the other vehicle is driving the wrong way,opposite to a flow of traffic, the other vehicle is driving outside ofdrivable lanes or on a gore area, and/or the other vehicle has cutacross multiple lanes of traffic without utilizing a turn signal.

In some embodiments, the processor is further configured to maintain thenon-compliant driver tag on the other vehicle for a predetermined lengthof time.

In some embodiments, the processor is further configured to cause theautonomous vehicle to minimize an amount of time that the autonomousvehicle drives and/or remains parallel to the other vehicle tagged asthe non-compliant driver.

In some embodiments, the processor is further configured to: determinethat the other vehicle is a speeding non-compliant driver in response tothe other vehicle moving at a predetermined speed above a speed limit;and in response to determining that the other vehicle is a speedingnon-compliant driver, cause the autonomous vehicle to avoid lanechanging into a lane of the other vehicle until the other vehicle haspassed the autonomous vehicle.

In some embodiments, the processor is further configured to: determinethat the other vehicle is an oscillating non-compliant driver inresponse to detecting that the other vehicle is swerving between both ofits lane lines; and in response to determining that the other vehicle isan oscillating non-compliant driver, cause the autonomous vehicle toavoid driving in a lane adjacent to the other vehicle.

In some embodiments, the processor is further configured to: determinethat the other vehicle is an intersection non-compliant driver inresponse to detecting that the other vehicle has proceeded into anintersection when the other vehicle does not have a right-of-way; and inresponse to determining that the other vehicle is an intersectionnon-compliant driver, cause the autonomous vehicle to yield aright-of-way to the other driver.

In some embodiments, the processor is further configured to cause theautonomous vehicle to avoid changing lanes towards the taggednon-compliant driver unless changing lanes is required to continue on aroute of the autonomous vehicle.

Another aspect includes a non-transitory computer-readable medium havingstored thereon instructions which, when executed by a processor, causethe processor to: determine that another vehicle is violating one ormore rules of a roadway based on perception data received from one ormore sensors of an autonomous vehicle; tag the other vehicle as anon-compliant driver; and modify control of the autonomous vehicle inresponse to tagging the other vehicle as a non-compliant driver.

In some embodiments, the instructions further cause the processor to:determine whether there is a potential for an accident between theautonomous vehicle and the other vehicle; and in response to determiningthat there is the potential for an accident between the autonomousvehicle and the other vehicle, determine an evasive maneuver by theautonomous vehicle to avoid or minimize any damage to the autonomousvehicle and other entities on or near the roadway.

In some embodiments, the instructions further cause the processor to:determine whether the potential for the accident is an emergencyscenario in which the accident is imminent; and cause the autonomousvehicle to execute the evasive maneuver in response to determining thatthe potential for the accident is the emergency scenario.

In some embodiments, the instructions further cause the processor to:calculate that a critical distance between the autonomous vehicle andthe other vehicle cannot be maintained even using a thresholddeceleration; and determine that the accident is imminent in response tocalculating that the critical distance between the autonomous vehicleand the other vehicle cannot be maintained even using the thresholddeceleration.

In some embodiments, the instructions further cause the processor tocause the autonomous vehicle to stay within its lane when executing anevasive maneuver unless evasive braking alone is not enough to prevent acollision.

In some embodiments, the instructions further cause the processor to:determine that a previously obstructed vehicle has been revealed due tothe other vehicle being removed as a source of obstruction, and inresponse to determining that the previously obstructed vehicle is nowunobstructed, cause the autonomous vehicle to brake up to a maximumamount of braking within safety limits to maintain a critical distancebetween the autonomous vehicle and the previously obstructed vehicle.

In some embodiments, the instructions further cause the processor to:determine that the other vehicle, in front of and in a same lane as theautonomous vehicle, has suddenly applied its brakes with a decelerationof greater than a predetermined amount of deceleration; and in responseto determining that that the other vehicle has suddenly applied itsbrakes, cause the autonomous vehicle to brake up to a maximum amount ofbraking within safety limits.

Another aspect is a method comprising: determining that another vehicleis violating one or more rules of a roadway based on perception datareceived from one or more sensors of an autonomous vehicle; tagging theother vehicle as a non-compliant driver; and modifying control of theautonomous vehicle in response to tagging the other vehicle as anon-compliant driver.

In some embodiments, the method further comprises: detecting that theautonomous vehicle has been involved in an accident; determining aseverity of the accident; and based on detecting that the autonomousvehicle has been involved in an accident and the determined severity,determine a course of action to minimize any further damage to theautonomous vehicle and/or any other entities on or near the roadway.

In some embodiments, detecting that the autonomous vehicle has beeninvolved in an accident is based on information received from one ormore inertial sensors, one or more cameras, and/or one or more lidars ofthe autonomous vehicle.

In some embodiments, the method further comprises: determining that theseverity of the accident is severe in response to a detecteddeceleration being greater than a threshold deceleration and/ordetermining that an object the autonomous vehicle has collided with is apedestrian, a cyclist, a motorcycle, and/or another vulnerable roaduser.

Another aspect is an autonomous vehicle configured to drive on aroadway, comprising: at least one perception sensor configured to detectroadway conditions data including roadway grade data; a processor; and anon-transitory computer readable medium configured to store mapped data,the mapped data having roadway grade data, and to store instructionsthat, when executed by the processor, cause the processor to: receivethe detected roadway conditions data including roadway grade data fromthe at least one perception sensor, retrieve the mapped data havingroadway grade data from the non-transitory computer readable medium,determine that the roadway has a grade based on the detected roadwaygrade data and the retrieved roadway grade data, in response todetermining that the roadway has a grade, determine that the grade ofthe roadway is greater than or equal to a predetermined high grade valueand less than a predetermined grade limit; and in response todetermining that the grade of the roadway is greater than or equal tothe predetermined high grade value and less than the predetermined gradelimit, operate the autonomous vehicle to change its lane to a right-mostlane.

In some embodiments, the predetermined grade limit is 7%.

In some embodiments, the predetermined grade limit is 9%.

In some embodiments, the predetermined high grade value is 5%.

In some embodiments, the predetermined high grade value is 7%.

In some embodiments, in response to determining that the grade of theroadway is greater than or equal to the predetermined grade limit, theprocessor is further configured to take a minimal risk condition (MRC)maneuver to stop the autonomous vehicle.

In some embodiments, the processor is further configured to stop theautonomous vehicle at a shoulder.

In some embodiments, in response to determining that the grade of theroadway is less than the predetermined high grade value, the processoris further configured to follow a speed limit indicated by a road gradetraffic sign detected by the at least one perception sensor or retrievedfrom the mapped data.

Another aspect is a method comprising: receiving detected roadwayconditions data including roadway grade data from at least oneperception sensor of an autonomous vehicle; retrieving mapped datahaving roadway grade data from a non-transitory computer readable mediumof the autonomous vehicle; determining that the roadway has a gradebased on the detected roadway grade data and the retrieved roadway gradedata; in response to determining that the roadway has a grade,determining that the grade of the roadway is greater than or equal to apredetermined high grade value and less than a predetermined gradelimit; and in response to determining that the grade of the roadway isgreater than or equal to the predetermined high grade value and lessthan the predetermined grade limit, operating the autonomous vehicle tochange its lane to a right-most lane.

In some embodiments, the method further comprises: determining there isa discrepancy between the detected roadway grade data and the retrievedroadway grade data; and in response to determining there is adiscrepancy between the detected roadway grade data and the retrievedroadway grade data, taking the detected roadway grade data as higherpriority over the retrieved roadway grade data.

In some embodiments, the autonomous vehicle is an autonomous truck.

In some embodiments, the detected roadway grade data includes a gradevalue, a grade sign including a positive sign or a negative sign, and agrade length.

In some embodiments, when the grade sign is determined to be negative,further comprising operating the autonomous vehicle to check brake.

In some embodiments, when the grade sign is determined to be negative,further comprising operating the autonomous vehicle to slow it down to apredetermined speed limit.

In some embodiments, when the grade sign is determined to be negative,further comprising operating the autonomous vehicle to change to a lowergear to slow down.

In some embodiments, when the grade sign is determined to be negative,further comprising operating the autonomous vehicle to change to itslowest gear to slow down.

In some embodiments, when the grade sign is determined to be negative,further comprising: determining that the roadway has an obstacle basedon the roadway conditions data from the at least one perception sensorof the autonomous vehicle; and in response to determining that theroadway has an obstacle, operating the autonomous vehicle to engage afoundation brake and change to a lower gear to stop the autonomousvehicle.

In some embodiments, when the grade sign is determined to be negative,further comprising: determining that the roadway has a truck turnoutbased on the roadway conditions data from the at least one perceptionsensor of the autonomous vehicle and the retrieved mapped data; and inresponse to determining that the roadway has a truck turnout, operatingthe autonomous vehicle to change lane and move into the truck turnout.

In some embodiments, when the grade sign is determined to be positive,further comprising increasing a throttle of the autonomous vehicle.

Another aspect is a non-transitory computer-readable medium havingstored thereon mapped data including roadway grade data and instructionswhich, when executed by a processor, cause the processor to: receivedetected roadway conditions data including roadway grade data from atleast one perception sensor of an autonomous vehicle; retrieve themapped data having roadway grade data from the non-transitorycomputer-readable medium; determine that the roadway has a grade basedon the detected roadway grade data and the retrieved roadway grade data;in response to determining that the roadway has a grade, determine thatthe grade of the roadway is greater than or equal to a predeterminedhigh grade value and less than a predetermined grade limit; and inresponse to determining that the grade of the roadway is greater than orequal to the predetermined high grade value and less than thepredetermined grade limit, operate the autonomous vehicle to change itslane to a right-most lane.

Another aspect is an autonomous vehicle comprising: at least oneperception sensor configured to generate perception data indicative of acondition of the environment; a network communication transceiverconfigured to communicate with an oversight system and receiveinformation from an external weather condition source; a non-transitorycomputer readable medium; and a processor configured to: receive theperception data from the at least one perception sensor, receive anindication of current weather conditions from the external weathercondition source, determine a current environmental condition severitylevel from a plurality of severity levels based on the perception dataand the indication of current weather conditions, modify one or moredriving parameters that govern a range of actions that can beautonomously executed by the autonomous vehicle, and navigate theautonomous vehicle based on the modified one or more driving parameters.

In some embodiments, the processor is further configured to: determine acurrent environmental condition severity level for each of a pluralityof different environmental conditions.

In some embodiments, the plurality of different environmental conditionsincludes two or more of: road traction, stability, or visibility.

In some embodiments, the processor is further configured to: determinethat at least two of the plurality of different environmental conditionshas a severity level other than normal, wherein modifying the one ormore driving parameters is further based on the determination that atleast two of the plurality of different environmental conditions have aseverity level other than normal.

In some embodiments, the plurality of severity levels comprises at leasttwo of: normal, degraded, cautionary, and critical.

In some embodiments, the processor is further configured to: determinethat the current environmental condition is critical, in response todetermining that the current environmental condition is critical, causethe autonomous vehicle to execute a minimal risk condition (MRC)maneuver.

In some embodiments, the MRC maneuver comprises pulling the autonomousvehicle over to a safe zone of a roadway.

In some embodiments, the processor is further configured to: determinethat the autonomous vehicle is operating out of an operational designdomain (ODD) of the autonomous vehicle, in response to determining thatthe autonomous vehicle is operating out of the ODD, determining that thecurrent environmental condition severity level is critical, and inresponse to determining that the current environmental condition iscritical, cause the autonomous vehicle to execute a first minimum riskcondition (MRC) maneuver.

In some embodiments, the processor is further configured to: determinethat the autonomous vehicle has been attempting the first MRC for longerthan a predetermined period of time without success, and in response todetermining that the autonomous vehicle has been attempting the firstMRC for longer than a predetermined period of time without success,cause the autonomous vehicle to execute a second MRC maneuver.

In some embodiments, the processor is further configured to: determinethat the current environmental condition severity level is degraded, andin response to determining that the current environmental conditionseverity level is degraded, modify the one or more driving parameters toinstruct the autonomous vehicle to change lanes to the right-most lanebased on determining that it is safe to perform lane changes.

In some embodiments, the processor is further configured to: determinethat the current environmental condition severity level is cautionary,and in response to determining that the current environmental conditionseverity level is cautionary, modify the one or more driving parametersto instruct the autonomous vehicle to avoid all lane changes, except forlane changes that are required for safety or lane changes required tocontinue the current mission.

Another aspect is a non-transitory computer-readable medium havingstored thereon instructions which, when executed by a processor, causethe processor to: receive perception data from at least one perceptionsensor of an autonomous vehicle; receive an indication of currentweather conditions from an external weather condition source; determinea current environmental condition severity level from a plurality ofseverity levels based on the perception data and the indication ofcurrent weather conditions; modify one or more driving parameters thatgovern a range of actions that can be autonomously executed by theautonomous vehicle; and navigate the autonomous vehicle based on themodified one or more driving parameters.

In some embodiments, the instructions further cause the processor to:determine a road traction coefficient based on the perception data; anddetermine that the current environmental condition severity level isdegraded in response to the road traction coefficient being less than athreshold value.

In some embodiments, the instructions further cause the processor to: inresponse to determining that the current environmental conditionseverity level is degraded: reduce a speed of the autonomous vehicle,perform lane changes with critical intent, apply a maximum availabledeceleration rate or less to decelerate, if safe to do so, lane changeto a right-most lane, and/or maintain a preference for the right-mostlane.

In some embodiments, the instructions further cause the processor to:determine that the autonomous vehicle has slowed down to a level below anormal speed but still greater than a threshold speed; and determinethat the current environmental condition severity level is degraded inresponse to determining that the autonomous vehicle has slowed down tothe level below the normal speed but still greater than the thresholdspeed.

In some embodiments, the instructions further cause the processor to:determine a road traction coefficient based on the perception data;determine that the autonomous vehicle has modified its behavior when theroad traction coefficient is less than a threshold value; and determinethat the current environmental condition severity level is degraded inresponse to determining that the autonomous vehicle has modified itsbehavior when the road traction coefficient is less than the thresholdvalue.

Another aspect is a method comprising: receiving perception data from atleast one perception sensor of an autonomous vehicle; receiving anindication of current weather conditions from an external weathercondition source; selecting a current environmental condition severitylevel from a plurality of severity levels based on the perception dataand the indication of current weather conditions; modifying one or moredriving parameters that govern a range of actions that can beautonomously executed by the autonomous vehicle; and navigating theautonomous vehicle based on the modified one or more driving parameters.

In some embodiments, the method of claim 57, further comprises:estimating a visibility range based on the perception data; determiningthat the current environmental condition is degraded in response to thevisibility range being less than a threshold value.

In some embodiments, the method further comprises: determining that theautonomous vehicle will enter a fog area based on the perception dataand the indication of current weather conditions; and activating lowbeams of the autonomous vehicle in response to determining that theautonomous vehicle will enter the fog area.

In some embodiments, the method further comprises: estimating a level ofwater based on road crown visibility from the perception data; andslowing down a speed of the autonomous vehicle in response to the levelof water being greater than a threshold level.

Another aspect is an autonomous vehicle configured to travel on aroadway, comprising: a trailer; at least one perception sensorconfigured to generate perception data indicative of: i) one or moreparameters of the roadway and ii) a movement of the autonomous vehicle;a processor; and a non-transitory computer readable medium having storedthereon instructions that, when executed by the processor, cause theprocessor to: estimate a grade of the roadway based on the perceptiondata indicative of the one or more parameters of the roadway, provide afirst control input to the autonomous vehicle based on the grade of theroadway, determine a response of the autonomous vehicle to the firstcontrol input based on the perception data indicative of the movement ofthe autonomous vehicle, estimate a trailer load of the trailer based onthe response of the autonomous vehicle to the first control input, andprovide a second control input to the autonomous vehicle based on thegrade of the roadway and the trailer load.

In some embodiments, the first control input comprises a throttle inputand/or a brake input.

In some embodiments, the processor is further configured to: determine awheel torque for one or more wheels of the autonomous vehicle, andestimate a mass of the trailer based on the determined wheel torque,wherein the second control input is further based at least in part onthe mass of the trailer.

In some embodiments, the processor is further configured to estimate aforce to move the trailer based on the wheel torque, wherein the secondcontrol input includes a throttle control and a brake control determinedbased on the force to move the trailer.

In some embodiments, the processor is further configured to determine aroad curvature in front of the autonomous vehicle with a distancegreater than a natural deceleration distance based on the perceptiondata indicative of the one or more parameters of the roadway, whereinthe second control input is further based on the road curvature.

In some embodiments, the processor is further configured to limit alateral acceleration of the autonomous vehicle in curves based on adistance to reduce a speed of the autonomous vehicle to limit a lateralacceleration taking into account braking capabilities of the autonomousvehicle.

In some embodiments, the processor is further configured to reduce aspeed of the autonomous vehicle to limit a lateral acceleration based ona maximum curvature of the road curvature.

In some embodiments, the second control input includes a throttlecontrol, and wherein the processor is further configured to dynamicallyadjust the throttle control based on the grade of the roadway and thetrailer load to provide a longitudinal control robustness for thethrottle control.

In some embodiments, the second control input includes a brake control,and wherein the processor is further configured to dynamically adjustthe brake control to compensate for the grade of the roadway and thetrailer load to provide a longitudinal control robustness for the brakecontrol.

In some embodiments, the second control input includes a steeringcontrol, and wherein the processor is further configured to dynamicallyadjust the steering control to compensate for side-wind effects and asuper elevation rate due to the trailer load to a provide longitudinalcontrol robustness for the steering control.

In some embodiments, the processor is further configured to limit alateral acceleration to a predetermined acceleration and a predeterminedjerk value to maintain a stability of the autonomous vehicle whenturning or driving on curved roads taking into account a super elevationrate, and wherein the processor is further configured to limit lateraldynamics for lateral maneuvers of the autonomous vehicle depending ontrailer inertia and stability criteria.

Another aspect is a non-transitory computer-readable medium havingstored thereon instructions which, when executed by a processor, causethe processor to: estimate a grade of a roadway based on perception dataindicative of one or more parameters of the roadway received from one ormore perception sensors of an autonomous vehicle; provide a firstcontrol input to the autonomous vehicle based on the grade of theroadway; determine a response of the autonomous vehicle to the firstcontrol input based on perception data indicative of a movement of theautonomous vehicle received from the one or more perception sensors;estimate a trailer load of the trailer based on the response of theautonomous vehicle to the first control input; and provide a secondcontrol input to the autonomous vehicle based on the grade of theroadway and the trailer load.

In some embodiments, the instructions further cause the processor toreduce a speed of the autonomous vehicle to maintain a lateralacceleration of the autonomous vehicle, and wherein the instructionsfurther cause the processor to limit a steering wheel angle velocity tolimit a lateral jerk of the autonomous vehicle.

In some embodiments, the instructions further cause the processor to:determine a type of the trailer load based on the response of theautonomous vehicle to the first control input; and cause the autonomousvehicle to accelerate up to a maximum acceleration that is based on thetrailer load and a maximum jerk that is based on the type of the trailerload.

In some embodiments, the instructions further cause the processor toensure that outermost points of the autonomous vehicle remain withininside edges of lane boundaries, unless the autonomous vehicle ischanging lanes, doing a critical safety bias, evading, turning at anintersection, and/or the autonomous vehicle is unable to remain withinthe lane boundaries due to a combination of a width of the lane and aroad curvature.

Another aspect is a method comprising: estimating a grade of a roadwaybased on perception data indicative of one or more parameters of theroadway received from one or more perception sensors of an autonomousvehicle; providing a first control input to the autonomous vehicle basedon the grade of the roadway; determining a response of the autonomousvehicle to the first control input based on perception data indicativeof a movement of the autonomous vehicle received from the one or moreperception sensors; estimating a trailer load of the trailer based onthe response of the autonomous vehicle to the first control input; andproviding a second control input to the autonomous vehicle based on thegrade of the roadway and the trailer load.

In some embodiments, the method further comprises: targeting a lateralposition in a lane of the autonomous vehicle such that widest points ofthe autonomous vehicle are substantially equidistant from laneboundaries when driving straight, turning, or changing lanes, unless foran evasive maneuver, bias, or to minimize off-tracking.

In some embodiments, the method further comprises: monitoring anydeviations from a targeted lateral position; and in response todetermining that the autonomous vehicle has deviated from the targetedlateral position by more than a predetermined deviation distance,returning to the targeted lateral position within a predetermineddeviation time.

In some embodiments, the method further comprises: detecting a schoolbus based on the perception data; detecting an extended stop sign arm ofthe school bus; and in response to detecting the extended stop sign arm,causing the autonomous vehicle to stop a predetermined distance awayfrom the school bus.

In some embodiments, the method further comprises: detecting an animalon the roadway based on the perception data; determining that the animalis larger than a predetermined size; in response to determining that theanimal is larger than a predetermined size, causing the autonomousvehicle to maintain a predetermined distance from the animal as theautonomous vehicle passes the animal.

Another aspect is an autonomous vehicle configured to drive on aroadway, comprising: at least one perception sensor configured togenerate perception data indicative of roadway conditions; at least oneglobal positioning system (GPS) receiving device configured to receive aGPS signal; a network communication subsystem configured to communicatewith a remote oversight system; a processor; and a non-transitorycomputer readable medium configured to store mapped data and havingstored thereon instructions that, when executed by the processor, causethe processor to: receive the perception data from the at least oneperception sensor, generate roadway condition data based on theperception data, receive the GPS signal from the at least one GPSreceiving device, retrieve the mapped data from the non-transitorycomputer readable medium, determine that the GPS signal meets a minimumlocalization accuracy requirement, combine the generated roadwaycondition data with the GPS signal to form detected road data, determinethat there is a discrepancy between the detected road data and theretrieved mapped data, in response to the determining that there is adiscrepancy between the detected road data and the retrieved mappeddata, update the mapped data with the detected road data, and transmitthe updated mapped data to the remote oversight system through thenetwork communication subsystem.

In some embodiments, the at least one GPS receiving device is aplurality of GPS receiving devices located on different parts of theautonomous vehicle to improve a strength of the received GPS signal anda positioning accuracy of the GPS signal.

In some embodiments, in response to determining that the received GPSdata does not meet the minimum localization accuracy requirement, theprocessor is further configured to cause the autonomous vehicle toperform a minimal risk condition (MRC) maneuver to stop the autonomousvehicle.

In some embodiments, the processor is further configured to cause theautonomous vehicle to stop at a shoulder of the roadway.

In some embodiments, the processor is further configured to communicateto the remote oversight system and request for assistance.

In some embodiments, the autonomous vehicle further comprises: a visualinertia odometry (VIO) configured to generate location data; and whereinwhen the received GPS signal does not meet the minimum localizationaccuracy requirement, the processor is further configured to: deriveroad positioning data from the VIO location data and the perception datareceived from the at least one perception sensor, and cause theautonomous vehicle to operate according to the derived road positioningdata.

In some embodiments, the derived road positioning data comprises a roadlongitudinal data and a road lateral data, and wherein the roadlongitudinal data is derived from the VIO location data, and the roadlateral data is derived from the perception data received from the atleast one perception sensor.

In some embodiments, the road lateral data is derived at least in partfrom perception of a lane marking by the at least one perception sensor.

In some embodiments, the processor is further configured to cause theautonomous vehicle to operate in a tunnel where the GPS signal isunavailable or is inaccurate.

In some embodiments, the processor is further configured to cause theautonomous vehicle to operate in a parking building where the GPS signaldoes not meet the minimum localization accuracy requirement.

Another aspect is a method comprising: receiving perception data from atleast one perception sensor of an autonomous vehicle; generating roadwaycondition data based on the perception data; receiving globalpositioning system (GPS) signal from at least one GPS receiving deviceof the autonomous vehicle; retrieving mapped data from a non-transitorycomputer readable medium of the autonomous vehicle; determining that theGPS signal meets a minimum localization accuracy requirement; combiningthe generated roadway condition data with the GPS signal to formdetected road data; determining that there is a discrepancy between thedetected road data and the retrieved mapped data; in response to thedetermining that there is a discrepancy between the detected road dataand the retrieved mapped data, updating the mapped data with thedetected road data; and transmitting the updated mapped data to theremote oversight system through a network communication subsystem.

In some embodiments, for each off-ramp exit on a limited access highwayin the digital map a plurality of safety areas is marked within 5 milesfrom the off-ramp exit.

In some embodiments, each of the plurality of safety areas is at least96 meters long and 3.4 meters wide.

In some embodiments, when the autonomous vehicle is a truck and aroadway is identified as restrictive to truck traffic, furthercomprising: finding a route excluding the roadway identified asrestricted to truck traffic.

In some embodiments, the roadway identified restrictive to truck trafficis a roadway having a weight limit that is lower than a weight of theautonomous vehicle.

In some embodiments, the roadway and/or a lane of the roadway identifiedas restrictive to truck traffic is a roadway and/or a lane of theroadway having a no-truck-traffic sign.

In some embodiments, when a construction zone traffic sign and trafficcontrol devices on a roadway are detected by the at least one perceptionsensor of the autonomous vehicle, further comprising: establishing avirtual wall separating the construction zone and the roadway; andcausing the autonomous vehicle to stay at least a predetermined safedistance away from the virtual wall.

In some embodiments, the predetermined safe distance is 8% of a lanewidth of the roadway.

In some embodiments, the traffic control devices include at least atraffic control barricade, a traffic control cone, a traffic controlbarrel, and a construction zone flagger.

Another aspect is a non-transitory computer-readable medium havingstored thereon mapped data and instructions which, when executed by aprocessor, cause the processor to: receive perception data from at leastone perception sensor of an autonomous vehicle; generate roadwaycondition data based on the perception data; retrieve mapped data fromthe non-transitory computer readable medium of the autonomous vehicle;determine that a global positioning system (GPS) signal from at leastone GPS receiving device of the autonomous vehicle meets a minimumlocalization accuracy requirement; combine the generated roadwaycondition data with the GPS signal to form a detected road data;determine that there is a discrepancy between the detected road data andthe retrieved mapped data; in response to the determining that there isa discrepancy between the detected road data and the retrieved mappeddata, update the mapped data with the detected road data; and transmitthe updated mapped data to a remote oversight system through a networkcommunication subsystem.

Another aspect is an autonomous vehicle comprising: at least oneperception sensor configured to generate perception data indicative ofphysical infrastructure on or near a roadway; a processor; and anon-transitory computer readable medium having stored thereoninstructions that, when executed by the processor, cause the processorto: determine a minimal risk condition (MRC) maneuver for the autonomousvehicle to execute, identify a safe zone in which the autonomous vehicleis able to execute the MRC maneuver by coming to a stop based at leastin part on the perception data, identify one or more exclusion zoneswithin the safe zone based on the perception data, and control theautonomous vehicle to execute the MRC maneuver including stoppingoutside of the one or more exclusion zones.

In some embodiments, the one or more exclusion zones comprise areas thatare within a predetermined distance from an emergency vehicle.

In some embodiments, controlling the autonomous vehicle to execute theMRC maneuver includes controlling the autonomous vehicle to avoidentering the one or more exclusion zones while executing the MRCmaneuver.

In some embodiments, the one or more exclusion zones comprise areas thatare within a predetermined distance from a construction zone.

In some embodiments, the processor is further configured to avoidcompleting the MRC maneuver within a threshold distance from a crosswalkat an intersection.

In some embodiments, the autonomous vehicle further comprises: a cabincluding a human machine interface (HMI), wherein the processor isfurther configured to provide a visual and/or audio notification via theHMI.

In some embodiments, the autonomous vehicle further comprising: hazardlights that are externally visible from the autonomous vehicle, whereincontrolling the autonomous vehicle to execute the MRC maneuver includes:controlling the autonomous vehicle to maneuver to a right-most lane, andin response to moving towards and/or reaching the right-most lane,activating the hazard lights.

In some embodiments, the processor is further configured to keep thehazard lights activated until deactivated by a human operator.

In some embodiments, the autonomous vehicle further comprises: aplurality of turn indicators, wherein the processor is furtherconfigured to set a usage of the plurality of turn indicators tosupersede a usage of the hazard lights while executing the MRC maneuver.

In some embodiments, the processor is further configured to: control theautonomous vehicle to maintain a minimum lateral distance between anouter most point of the autonomous vehicle and a lane line thatseparates a driving lane from the safe zone at a closest point ofapproach when the autonomous vehicle comes to a stop during the MRCmaneuver.

In some embodiments, controlling the autonomous vehicle to execute theMRC maneuver includes controlling the autonomous vehicle to come to astop when the autonomous vehicle is completely within the safe zone andno part of the autonomous vehicle is intruding into a driving lane.

In some embodiments, the processor is further configured to cause theautonomous vehicle to yield to any approaching emergency vehicle thathas activated its siren and/or emergency lights while the autonomousvehicle is executing the MRC maneuver.

In some embodiments, determining that the autonomous vehicle is toexecute the MRC maneuver is further in response to determining that theautonomous vehicle is approaching a boundary of a map.

Another aspect is a non-transitory computer-readable medium havingstored thereon instructions which, when executed by a processor, causethe processor to: determine a minimal risk condition (MRC) maneuver foran autonomous vehicle to execute; identify a safe zone in which theautonomous vehicle is able to execute the MRC maneuver by coming to astop based on perception data received from at least one perceptionsensor of the autonomous vehicle, the at least one perception sensorconfigured to generate the perception data to be indicative of physicalinfrastructure on or near a roadway; identify one or more exclusionzones within the safe zone based on the perception data; and control theautonomous vehicle to execute the MRC maneuver including stoppingoutside of the one or more exclusion zones.

In some embodiments, the instructions further cause the processor toreduce speed of the autonomous vehicle to maintain a lateralacceleration of the autonomous vehicle.

In some embodiments, the instructions further cause the processor to:detect an equipment failure of the autonomous vehicle based on theperception data; determine a severity level of the equipment failure;and determine whether the autonomous vehicle is to execute the MRCmaneuver or plan to visit a service station based on the severity level.

In some embodiments, the instructions further cause the processor to:determine that the severity level is less than a predetermined level;determine that the service station is within a predetermined distancefrom the autonomous vehicle based on the perception data; and determinea re-routing for the autonomous vehicle to visit the service station inresponse to determining that the severity level is less than thepredetermined level, and determining that the service station is withinthe predetermined distance of the autonomous vehicle.

Another aspect is a method comprising: determining a minimal riskcondition (MRC) maneuver for an autonomous vehicle to execute;identifying a safe zone in which the autonomous vehicle is able toexecute the MRC maneuver by coming to a stop based on perception datareceived from at least one perception sensor of the autonomous vehicle,the at least one perception sensor configured to generate the perceptiondata to be indicative of physical infrastructure on or near a roadway;identifying one or more exclusion zones within the safe zone based onthe perception data; and controlling the autonomous vehicle to executethe MRC maneuver including stopping outside of the one or more exclusionzones.

In some embodiments, method further comprises: detecting a verticalclearance on a current path of the autonomous vehicle based on theperception data; and determining that the vertical clearance is greaterthan a vertical-clearance threshold value, wherein determining that theautonomous vehicle is to execute the MRC maneuver is further in responseto determining that the vertical clearance is greater than the verticalclearance threshold value.

In some embodiments, the method further comprises: detecting atoll-booth facility based on the perception data; identifying a startpoint of the toll-booth facility based on detecting where highway linesstart to spread out into a plurality of toll lanes; and identifying anend point of the toll-booth facility based on detecting where theplurality of toll lanes starts to merge back into the highway lanes.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 illustrates a schematic diagram of a system including anautonomous vehicle;

FIG. 2 shows a flow diagram for operation of an autonomous vehicle (AV)safely in light of the health and surroundings of the AV; and

FIG. 3 illustrates a system that includes one or more autonomousvehicles, a control center or oversight system with a human operator(e.g., a remote center operator (RCO)), and an interface for third-partyinteraction.

FIG. 4A illustrates an example visualization of a fast reveal scenarioin accordance with aspects of this disclosure.

FIGS. 4B-4C illustrate example visualizations of sudden brakingscenarios in accordance with aspects of this disclosure.

FIG. 4D illustrates an example visualization of a lateral intrusionscenario in accordance with aspects of this disclosure.

FIG. 4E illustrates an example visualization of a cross path scenario inaccordance with aspects of this disclosure.

FIG. 4F illustrates an example visualization of an oncoming scenario inaccordance with aspects of this disclosure.

FIG. 4G illustrates an example visualization of a cut-in scenario inaccordance with aspects of this disclosure.

FIG. 4H illustrates an example visualization of a non-compliant vehicleparallel to the autonomous vehicle in accordance with aspects of thisdisclosure.

FIG. 4I illustrates an example visualization of a speeding non-compliantvehicle in accordance with aspects of this disclosure.

FIG. 4J illustrates an example visualization of an oscillatingnon-compliant vehicle in accordance with aspects of this disclosure.

FIG. 4K illustrates an example visualization of an intersectionnon-compliant vehicle in accordance with aspects of this disclosure.

FIG. 4L illustrates an example visualization of a bumper-to-bumper gapand a lateral gap in accordance with aspects of this disclosure.

FIG. 4M illustrates an example method which can be used to control theautonomous vehicle based on the detection of a non-compliant vehicle.

FIG. 5A is a photo showing an example of a level crossing.

FIG. 5B is shows an example speed limit traffic sign.

FIG. 5C is shows an example do-not-pass traffic sign.

FIG. 5D is a schematic illustrating definition of a roadway grade.

FIG. 5E-FIG. 5L are photos showing different examples of grade trafficsigns and driving advice signs.

FIG. 5M-FIG. 5Q are more examples of driving advice with grade signs.

FIG. 5R is schematic illustrating the definition of a superelevationroad.

FIG. 5S is a block diagram illustrating the steps of driving through asuperelevation road.

FIG. 5T-FIG. 5V show examples of road blockage traffic signs.

FIG. 5W-FIG. 5Y show examples of road blockage devices.

FIG. 5Z shows an example construction zone traffic sign by a roadside.

FIG. 5AA shows another example construction zone traffic sign.

FIG. 5AB shows a photo presenting an example traffic sign and trafficcontrol devices (barricades) combination which define a virtual wall fora construction zone.

FIG. 5AC shows another photo presenting an example traffic sign andtraffic control devices (barrels) combination which define a virtualwall for a construction zone.

FIG. 5AD and FIG. 5AE are schematics depicting example construction zoneflaggers presenting stop and proceed signals.

FIG. 5AF-FIG. 5AJ are schematics illustrating traffic blockage andcontrol devices.

FIG. 5AK is an example construction zone traffic sign.

FIG. 5AL is a photo showing speed limit reduction in a constructionzone.

FIG. 5AM and FIG. 5AN are photos showing human traffic control(flaggers).

FIG. 5AO-FIG. 5AT are schematics for human traffic control (flaggers).

FIG. 5AU shows a roadway schematic having solid white lines illustratinglane closure at a construction zone.

FIG. 5AV is an example of a be-prepared-to-stop traffic signillustrating lane closure at a construction zone.

FIG. 5AW shows a roadway schematic illustrating lane shift in aconstruction zone.

FIG. 5AX-FIG. 5BA are example traffic signs illustrating lane shift in aconstruction zone.

FIG. 5BB shows an example truck crossing traffic sign.

FIG. 5BC is a plan view schematic illustrating a diverging diamondinterchange on a highway system.

FIG. 5BD is a plan view photo showing a full cloverleaf interchange on ahighway system.

FIG. 5BE is a bird's-eye view photo showing an interchange on a highwaysystem.

FIG. 5BF is a table containing roundabout category data.

FIG. 5BG is an example mini roundabout traffic sign.

FIGS. 5BH-2BK show example roundabout traffic signs.

FIG. 5BL is a photo showing a two-way left turn lane (center lane).

FIG. 5BM and FIG. 5BN are two example two-way left turn lane trafficsigns.

FIG. 5BO is a plan view schematic showing a two-way left turn lane inthe middle of a roadway.

FIG. 5BP is a plan view schematic illustrating possible entry pointsinto a two-way left turn lane in a roadway.

FIG. 5BQ shows an example traffic light traffic sign.

FIGS. 5BR-5BT are examples of intersection traffic signs.

FIG. 5BU and FIG. 5BV show no-turn-on-red and left-turn-yield-on-greentraffic signs at intersections with traffic lights.

FIG. 5BY is a street photo showing part of a truck apron at a roundaboutintersection.

FIG. 5BZ and FIG. 5CA are two plan schematics illustrating trafficcircle intersections.

FIG. 5CB is a photo showing a fork road intersection.

FIG. 5CC is a plan view schematic illustrating an uncontrolledintersection.

FIG. 5CD is a plan view schematic illustrating a four-way intersection.

FIGS. 5CE-5CH show a plurality of crosswalk traffic signs as examples.

FIGS. 5CI-5CM show a plurality of crosswalk traffic signs as moreexamples.

FIG. 5CN is a plan view schematic illustrating an example dynamic zonein a roadway.

FIG. 5CO is a plan view schematic illustrating another example dynamiczone at an intersection.

FIGS. 5CP-5CT show photos depicting different lane boundaries.

FIG. 5CU is a photo showing lane restriction by traffic signal.

FIG. 5CV is a photo showing lane restriction by movable physicalbarrier.

FIGS. 5CW-5DB are example traffic signs indicating fixed zones.

FIG. 5DC is a photo showing a lighted street in the night.

FIG. 5DD is a photo showing the inside of a tunnel with lighting.

FIG. 5DE and FIG. 5DF are plan view schematics showing entry ramps onhighways.

FIGS. 5DG-5DI are plan view schematics showing exit ramps on highways.

FIG. 5DJ is a plan view schematic illustrating a cloverleaf ramp on ahighway.

FIG. 5DK is a photo showing an entry ramp traffic sign standing at aroad boundary.

FIG. 5DL is a plan view schematic illustrating a parallel lane as partof an entry ramp on a highway.

FIG. 5DM is a plan view schematic illustrating an exit ramp on ahighway.

FIG. 5DN is an example technique for operating an autonomous vehicle ona roadway with a grade.

FIGS. 6A and 6B illustrate example visualizations of the periodicallyupdated map in accordance with aspects of this disclosure.

FIGS. 6C and 6D illustrate example visualizations of the safe zones onor near the roadway in accordance with aspects of this disclosure.

FIGS. 6E-6G illustrate example visualizations of traffic signs that mayidentify icy conditions in accordance with aspects of this disclosure.

FIG. 6H illustrates an example visualization of a traffic sign that mayidentify a fog area in accordance with aspects of this disclosure.

FIGS. 6I-6K illustrate example visualizations of lighting zones inaccordance with aspects of this disclosure.

FIGS. 6L-6O illustrate example visualizations of traffic signs that mayidentify that the use of headlights is appropriate in accordance withaspects of this disclosure.

FIGS. 6P-6R illustrate example visualizations of traffic signs that mayidentify that the use of headlights is required in accordance withaspects of this disclosure.

FIG. 6S illustrates an example visualization of one or more entitieswhich are within the autonomous vehicle's lighting zone in accordancewith aspects of this disclosure.

FIG. 6T illustrates an example method which can be used to control theautonomous vehicle taking into account environmental conditions detectedby at least one perception sensor.

FIG. 7A illustrates an example visualization of the forces (e.g.,lateral resistive forces) which may be relevant to an autonomous vehicledriving on a roadway with an incline.

FIGS. 7B-7C illustrate example visualizations of yield signs.

FIGS. 7D-7E illustrate example visualizations of no turn signs.

FIG. 7F illustrates an example visualization of a no right turn sign.

FIGS. 7G-7I illustrate example visualizations of signs which have rightturn only lanes.

FIGS. 7J-K illustrate example visualizations of no left turn signs.

FIGS. 7L-7M illustrate example visualizations of signs which have leftturn only lanes.

FIG. 7N illustrates an example visualization of a no trucks sign.

FIGS. 70-7V illustrate example visualizations of pedestrian crossingsigns.

FIG. 7W illustrates an example visualization of a truck route sign.

FIGS. 7X-7AC illustrate example visualizations of weight limit signs.

FIGS. 7AD-7AF illustrate example visualizations of road closure signs.

FIGS. 7AG-7AK illustrate example visualizations of mandatory freewayexit signs.

FIGS. 7AL-7AZ illustrate example visualizations of environmentprecaution signs.

FIGS. 7BA-7BB illustrate example visualizations of reduced speed limitahead signs.

FIGS. 7BC-7BJ illustrate example visualizations of merging signs.

FIGS. 7BK-7BT illustrate example visualizations of non-vehicular warningsigns.

FIGS. 7BU-BX illustrate example visualizations of advanced turn signs.

FIG. 7BY illustrates an example method which can be used to control theautonomous vehicle based on the estimation of the trailer load.

FIG. 8A is a photo showing an example limited access highway.

FIG. 8B is a photo showing an example single-lane road.

FIG. 8C is a photo showing an example multi-lane road.

FIG. 8D is a photo showing an example median between the opposingdirection lanes of a highway.

FIG. 8E is a photo of an example one-way road where traffic drives onlyin a single direction as indicated by a traffic sign.

FIG. 8F schematically illustrates an example frontage road in paralleland connected to a highway.

FIG. 8G schematically illustrates an example on-ramp from an undividedarterial road to a limited access highway.

FIG. 8H is a photo showing an example off-ramp to allow a vehicle toexit a limited access highway.

FIG. 8I schematically illustrates a gore area between the outside laneof a highway and an on-ramp.

FIG. 8J is a photo showing an example merge area starting at a gorepoint and finishing when the merged lane boundaries are the same widthas the surrounding lanes.

FIG. 8K is a photo showing an example local merge area indicated bytraffic signs “left lane ends” and “merge right”.

FIG. 8L schematically illustrates a roadway with an emergency laneoutside of drivable lanes as part of a map extension.

FIG. 8M is a photo showing an example of a part time shoulder lane on adivided highway as indicated by an arrow light.

FIGS. 8N-8Q are examples of different traffic signs to indicate that anemergency lane can be used as an evaculane for different evacuationpurposes.

FIG. 8R is a photo showing an example intersection.

FIG. 8S is a photo showing an example of a temporary merge lane justbefore an off-ramp.

FIG. 8T is a photo showing an example express lane as indicated by atraffic sign.

FIG. 8U is a photo showing an example high-occupancy vehicle (HOV) laneas indicated by a traffic sign.

FIG. 8V is a photo showing an HOV lane entrance on a highway.

FIG. 8W is a photo showing a high-occupancy toll (HOT) road entrancestation available to high-occupancy vehicles and other vehicles that paya toll to use.

FIG. 8X schematically illustrates a center lane located in the middle ofa two-way street, in which traffic may travel in either direction andmake left turns.

FIG. 8Y is a photo showing an example of a truck lane.

FIG. 8Z is a photo showing an example of a bicycle lane.

FIG. 8AA is a photo showing an example of bus lanes.

FIG. 8AB schematically illustrates a slip lane at an intersection.

FIG. 8AC is a photo showing an example of a slip lane at anintersection.

FIG. 8AD is a schematic showing a merge through lane and a merge endinglane.

FIG. 8AE schematically illustrates a sidewalk defined between a curb anda boundary.

FIG. 8AF is a photo showing a crosswalk with spaced zebra markingsintersecting a driveway.

FIG. 8AG is a photo showing an example of a tunnel with an entrance.

FIG. 8AH is a photo showing an example driveway from a local roadway toa gas station.

FIG. 8AI is a photo showing a no trucks lane on a highway.

FIG. 8AJ and FIG. 8AK show examples of one-way traffic signs.

FIG. 8AL and FIG. 8AM show examples of uneven road surface trafficsigns.

FIG. 8AN shows an example of a soft shoulder traffic sign.

FIGS. 8AO-8AR are examples of traffic signs for road surface conditions.

FIG. 8AS shows an example of a falling rocks on road traffic sign.

FIG. 8AT and FIG. 8AU are photos showing two example zone flaggers, eachholding a stop/slow paddle.

FIG. 8AV is a photo showing a construction zone flagger using a “stoproad users” hand signal to stop oncoming vehicles.

FIG. 8AW is a schematic showing a construction zone flagger using a“proceed road users” hand signal to allow oncoming vehicles to moveforward.

FIG. 8AX is a photo showing a construction zone flagger using a “proceedroad users” hand signal to allow oncoming vehicles to move forward.

FIG. 8AY is a schematic showing a construction zone flagger using a“slow road users” hand signal to slow down oncoming vehicles.

FIG. 8AZ is a photo showing a construction zone flagger using a “slowroad users” hand signal to slow down oncoming vehicles.

FIGS. 8BA-8BD are examples of construction zone traffic signs.

FIGS. 8BE-8BI are examples of lane closure traffic signs.

FIG. 8BJ shows an example of a detour traffic sign.

FIGS. 8BK-8BM show examples of speed limit signs together with othertraffic signs.

FIGS. 8BN-8BP show examples of end of construction zone traffic signs.

FIGS. 8BQ-8BS show examples of construction zone flagger's hand signaldevices including stop/slow paddles and a flag.

FIGS. 8BT-8CB schematically present construction zone flagger's handsignaling.

FIGS. 8CC-8CG show weight limitation traffic signs.

FIGS. 8CH-8CQ show ongoing road construction traffic signs.

FIG. 8CR is a photo showing a road narrowing traffic sign standing by aroadside.

FIGS. 8CS-8CU are photos showing examples of road restriction signs.

FIGS. 8CV-8CZ are photos showing examples of traffic control devices.

FIG. 8DA shows an example of a merge traffic sign.

FIG. 8DB is a block diagram showing an example technique for updatingthe digital map based on real time data from an autonomous vehicle.

FIG. 9A illustrates an example visualization of a first MRC maneuver foran example scenario in where there are no external complications.

FIG. 9B illustrates an example visualization of a first MRC maneuver fora first example scenario in which the autonomous vehicle is performing aleft lane change.

FIG. 9C illustrates an example visualization of a first MRC maneuver fora second example scenario in which the autonomous vehicle is performinga left lane change.

FIG. 9D illustrates an example visualization of a first MRC maneuver foran example scenario in which the autonomous vehicle is performing aright lane change.

FIG. 9E illustrates an example visualization of a first MRC maneuver foran example scenario in which the safe area is taken or occupied.

FIG. 9F illustrates an example visualization of a first MRC maneuver foran example scenario in which an ELV is located in the safe area.

FIG. 9G illustrates an example visualization of a first MRC maneuver foran example scenario in which the autonomous vehicle is approaching a mapboundary.

FIG. 9H illustrates an example visualization of a first MRC maneuver foran example scenario in which the autonomous vehicle has missed an exit.

FIG. 9I illustrates an example visualization of a first MRC maneuver foran example scenario in which the autonomous vehicle has been forced offroute.

FIGS. 9J-9L illustrate example visualizations of service station signs.

FIGS. 9M-9N illustrate example visualizations of service station signsindicating that a service station is either open or closed.

FIG. 90 illustrates an example visualization of a service station thatcan include one or more signs having instructions.

FIGS. 9P-9R illustrate example visualizations of signs that can belocated in a service station.

FIG. 9S illustrates an example visualization of one or more queues at aservice station.

FIG. 9T illustrates an example visualization of a bridge which has avertical clearance.

FIGS. 9U-9X illustrate example visualizations of signs that may indicatethe presence of a low clearance area or object ahead.

FIGS. 9Y-9AB illustrate example visualizations of signs that mayindicate the presence of a weigh station ahead.

FIGS. 9AC-9AE illustrate example visualizations of signs and signalsthat may indicate whether a weigh station is open or closed.

FIGS. 9AF-9AI illustrate example visualizations of toll boothfacilities.

FIGS. 9AJ-9AL illustrate example visualizations of toll booth facilitiesincluding traffic signs indicating whether the corresponding toll boothsare open or closed.

FIGS. 9AM-9AP illustrate example visualizations of a map and signs thatindicate the presence of toll booths.

FIGS. 9AQ-9AR illustrate example visualizations of signs indicating thetoll payment types accepted by a corresponding toll lane.

FIG. 9AS illustrates an example visualization of a front distancebetween the autonomous vehicle and a target lane front vehicle.

FIG. 9AT illustrates an example visualization of a toll booth facility.

FIG. 9AU illustrates an example visualization of a ramp with a rampmeter.

FIGS. 9AV-9AZ illustrate example visualizations of signs that canindicate the presence of a ramp meter.

FIGS. 9BA-9BC illustrate example visualizations of signs that canindicate flow control schemes.

FIGS. 9BD-9BE illustrate example visualizations of signs and/or flashinglights that can indicate the presence of a ramp meter.

FIGS. 9BF-9B1 illustrate example visualizations of signs and/or flashinglights that can indicate the presence of ramp meter traffic lights andflow control schemes.

FIG. 9BJ illustrates an example visualization of traffic stopped at aramp meter.

FIG. 9BK illustrates an example visualization of merging traffic.

FIG. 9BL illustrates an example visualization of a speed bump.

FIGS. 9BM-9BO illustrate example visualizations of speed humps.

FIG. 9BP illustrates an example visualization of a speed cushion.

FIG. 9BQ illustrates an example visualization of a speed table.

FIG. 9BR illustrates an example visualization of a portable speed bump.

FIG. 9BS illustrates an example visualization of a sign indicating thepresence of a speed hump.

FIG. 9BT illustrates an example visualization of a speed bump and a signindicating a speed limit for the speed bump.

FIG. 9BU illustrates an example visualization of a speed table with across walk.

FIG. 9BV illustrates an example visualization of a pothole.

FIG. 9BW illustrates an example visualization of a roadway withshoulders.

FIG. 9BX illustrates an example visualization of a highway with ashoulder.

FIGS. 9BY-9CA illustrate example visualizations of roadways havingdifferent widths.

FIGS. 9CB-9CD illustrate example visualizations of signs that indicatewhether trucks are permitted in a given lane or roadway.

FIG. 9CE illustrates an example visualization of the vertical clearanceof a tunnel.

FIGS. 9CF-9CG illustrate example visualizations of a tunnel and signsthat indicate the vertical clearance for tunnels.

FIG. 9CH illustrates an example visualization of a sign indicating thatheadlight should be used in the upcoming tunnel.

FIG. 9CI illustrates an example method which can be used to control theautonomous vehicle during an MRC maneuver.

DETAILED DESCRIPTION

Vehicles traversing highways and roadways are legally required to complywith regulations and statutes in the course of safe operation of thevehicle. For autonomous vehicles (AVs), particularly autonomous tractortrailers, the ability to recognize a malfunction in its systems and stopsafely are necessary for lawful and safe operation of the vehicle.Described below in detail are systems and methods for the safe andlawful operation of an autonomous vehicle on a roadway, including theexecution of maneuvers that bring the autonomous vehicle in compliancewith the law while signaling surrounding vehicles of its condition.

Overview of Autonomous Vehicles

FIG. 1 shows a system 100 that includes a tractor 105 of an autonomoustruck. The tractor 105 includes a plurality of vehicle subsystems 140and an in-vehicle control computer 150. The plurality of vehiclesubsystems 140 includes vehicle drive subsystems 142, vehicle sensorsubsystems 144, and vehicle control subsystems. An engine or motor,wheels and tires, a transmission, an electrical subsystem, and a powersubsystem may be included in the vehicle drive subsystems. The engine ofthe autonomous truck may be an internal combustion engine, a fuel-cellpowered electric engine, a battery powered electrical engine, a hybridengine, or any other type of engine capable of moving the wheels onwhich the tractor 105 moves. The tractor 105 may have multiple motors oractuators to drive the wheels of the vehicle, such that the vehicledrive subsystems 142 include two or more electrically driven motors. Thetransmission may include a continuous variable transmission or a setnumber of gears that translate the power created by the engine into aforce that drives the wheels of the vehicle. The vehicle drivesubsystems may include an electrical system that monitors and controlsthe distribution of electrical current to components within the system,including pumps, fans, and actuators. The power subsystem of the vehicledrive subsystem may include components that regulate the power source ofthe vehicle.

Vehicle sensor subsystems 144 can include sensors for general operationof the autonomous truck 105, including those which would indicate amalfunction in the AV or another cause for an AV to perform a limited orminimal risk condition (MRC) maneuver. The sensors for general operationof the autonomous vehicle may include cameras, a temperature sensor, aninertial sensor (IMU), a global positioning system, a light sensor, aLIDAR system, a radar system, and wireless communications.

A sound detection array, such as a microphone or array of microphones,may be included in the vehicle sensor subsystem 144. The microphones ofthe sound detection array are configured to receive audio indications ofthe presence of, or instructions from, authorities, including sirens andcommand such as “Pull over.” These microphones are mounted, or located,on the external portion of the vehicle, specifically on the outside ofthe tractor portion of an autonomous truck 105. Microphones used may beany suitable type, mounted such that they are effective both when theautonomous truck 105 is at rest, as well as when it is moving at normaldriving speeds.

Cameras included in the vehicle sensor subsystems 144 may be rear facingso that flashing lights from emergency vehicles may be observed from allaround the autonomous truck 105. These cameras may include videocameras, cameras with filters for specific wavelengths, as well as anyother cameras suitable to detect emergency vehicle lights based oncolor, flashing, of both color and flashing.

The vehicle control subsystem 146 may be configured to control operationof the autonomous vehicle, or truck, 105 and its components.Accordingly, the vehicle control subsystem 146 may include variouselements such as an engine power output subsystem, a brake unit, anavigation unit, a steering system, and an autonomous control unit. Theengine power output may control the operation of the engine, includingthe torque produced or horsepower provided, as well as provide controlthe gear selection of the transmission. The brake unit can include anycombination of mechanisms configured to decelerate the autonomousvehicle 105. The brake unit can use friction to slow the wheels in astandard manner. The brake unit may include an Anti-lock brake system(ABS) that can prevent the brakes from locking up when the brakes areapplied. The navigation unit may be any system configured to determine adriving path or route for the autonomous vehicle 105. The navigationunit may additionally be configured to update the driving pathdynamically while the autonomous vehicle 105 is in operation. In someembodiments, the navigation unit may be configured to incorporate datafrom the GPS device and one or more predetermined maps so as todetermine the driving path for the autonomous vehicle 105. The steeringsystem may represent any combination of mechanisms that may be operableto adjust the heading of autonomous vehicle 105 in an autonomous mode orin a driver-controlled mode.

The autonomous control unit may represent a control system configured toidentify, evaluate, and avoid or otherwise negotiate potential obstaclesin the environment of the autonomous vehicle 105. In general, theautonomous control unit may be configured to control the autonomousvehicle 105 for operation without a driver or to provide driverassistance in controlling the autonomous vehicle 105. In someembodiments, the autonomous control unit may be configured toincorporate data from the GPS device, the RADAR, the LiDAR (i.e.,LIDAR), the cameras, and/or other vehicle subsystems to determine thedriving path or trajectory for the autonomous vehicle 105. Theautonomous control that may activate systems that the AV 105 has whichare not present in a conventional vehicle, including those systems whichcan allow an AV to communicate with surrounding drivers or signalsurrounding vehicles or drivers for safe operation of the AV.

An in-vehicle control computer 150, which may be referred to as a VCU,includes a vehicle subsystem interface 160, a driving operation module168, one or more processors 170, a compliance module 166, a memory 175,and a network communications subsystem 178. This in-vehicle controlcomputer 150 controls many, if not all, of the operations of theautonomous truck 105 in response to information from the various vehiclesubsystems 140. The one or more processors 170 execute the operationsthat allow the system to determine the health of the AV, such as whetherthe AV has a malfunction or has encountered a situation requiringservice or a deviation from normal operation and giving instructions.Data from the vehicle sensor subsystems 144 is provided to VCU 150 sothat the determination of the status of the AV can be made. Thecompliance module 166 determines what action should be taken by theautonomous truck 105 to operate according to the applicable (i.e.,local) regulations. Data from other vehicle sensor subsystems 144 may beprovided to the compliance module 166 so that the best course of actionin light of the AV's status may be appropriately determined andperformed. Alternatively, or additionally, the compliance module 166 maydetermine the course of action in conjunction with another operationalor control module, such as the driving operation module 168.

The memory 175 may contain additional instructions as well, includinginstructions to transmit data to, receive data from, interact with, orcontrol one or more of the vehicle drive subsystems 142, the vehiclesensor subsystem 144, and the vehicle control subsystem 146 includingthe autonomous Control system. The in-vehicle control computer (VCU) 150may control the function of the autonomous vehicle 105 based on inputsreceived from various vehicle subsystems (e.g., the vehicle drivesubsystems 142, the vehicle sensor subsystem 144, and the vehiclecontrol subsystem 146). Additionally, the VCU 150 may send informationto the vehicle control subsystems 146 to direct the trajectory,velocity, signaling behaviors, and the like, of the autonomous vehicle105. The autonomous control vehicle control subsystem may receive acourse of action to be taken from the compliance module 166 of the VCU150 and consequently relay instructions to other subsystems to executethe course of action.

FIG. 2 shows a flow diagram for operation of an autonomous vehicle (AV)safely in light of the health and surroundings of the AV. Although thisfigure depicts functional steps in a particular order for purposes ofillustration, the process is not limited to any particular order orarrangement of steps. One skilled in the relevant art will appreciatethat the various steps portrayed in this figure could be omitted,rearranged, combined and/or adapted in various ways.

As shown in FIG. 2 , the vehicle sensor subsystem 144 receives visual,auditory, or both visual and auditory signals indicating the at theenvironmental condition of the AV, as well as vehicle health or sensoractivity data are received in step 205. These visual and/or auditorysignal data are transmitted from the vehicle sensor subsystem 144 to thein-vehicle control computer system (VCU) 150, as in step 210. Any of thedriving operation module and the compliance module receive the datatransmitted from the vehicle sensor subsystem, in step 215. Then, one orboth of those modules determine whether the current status of the AV canallow it to proceed in the usual manner or that the AV needs to alterits course to prevent damage or injury or to allow for service in step220. The information indicating that a change to the course of the AV isneeded may include an indicator of sensor malfunction; an indicator of amalfunction in the engine, brakes, or other components necessary for theoperation of the autonomous vehicle; a determination of a visualinstruction from authorities such as flares, cones, or signage; adetermination of authority personnel present on the roadway; adetermination of a law enforcement vehicle on the roadway approachingthe autonomous vehicle, including from which direction; and adetermination of a law enforcement or first responder vehicle movingaway from or on a separate roadway from the autonomous vehicle. Thisinformation indicating that a change to the AV's course of action isneeded may be used by the compliance module to formulate a new course ofaction to be taken which accounts for the AV's health and surroundings,in step 225. The course of action to be taken may include slowing,stopping, moving into a shoulder, changing route, changing lane whilestaying on the same general route, and the like. The course of action tobe taken may include initiating communications with any oversight orhuman interaction systems present on the autonomous vehicle. The courseof action to be taken may then be transmitted from the VCU 150 to theautonomous control system, in step 230. The vehicle control subsystems146 then cause the autonomous truck 105 to operate in accordance withthe course of action to be taken that was received from the VCU 150 instep 235.

It should be understood that the specific order or hierarchy of steps inthe processes disclosed herein is an example of exemplary approaches.Based upon design preferences, it is understood that the specific orderor hierarchy of steps in the processes may be rearranged while remainingwithin the scope of the present disclosure. The accompanying methodclaims present elements of the various steps in a sample order and arenot meant to be limited to the specific order or hierarchy presented.

Autonomous Truck Oversight System

FIG. 3 illustrates a system 300 that includes one or more autonomousvehicles 105, a control center or oversight system 350 with a humanoperator 355, and an interface 362 for third-party 360 interaction. Ahuman operator 355 may also be known as a remoter center operator (RCO).Communications between the autonomous vehicles 105, oversight system 350and user interface 362 take place over a network 370. In some instances,where not all the autonomous vehicles 105 in a fleet are able tocommunicate with the oversight system 350, the autonomous vehicles 105may communicate with each other over the network 370 or directly. Asdescribed with respect to FIG. 1 , the VCU 150 of each autonomousvehicle 105 may include a module for network communications 178.

An autonomous truck may be in communication with an oversight system.The oversight system may serve many purposes, including: tracking theprogress of one or more autonomous vehicles (e.g., an autonomous truck);tracking the progress of a fleet of autonomous vehicles; sendingmaneuvering instructions to one or more autonomous vehicles; monitoringthe health of the autonomous vehicle(s); monitoring the status of thecargo of each autonomous vehicle in contact with the oversight system;facilitate communications between third parties (e.g., law enforcement,clients whose cargo is being carried) and each, or a specific,autonomous vehicle; allow for tracking of specific autonomous trucks incommunication with the oversight system (e.g., third-party tracking of asubset of vehicles in a fleet); arranging maintenance service for theautonomous vehicles (e.g., oil changing, fueling, maintaining the levelsof other fluids); alerting an affected autonomous vehicle of changes intraffic or weather that may adversely impact a route or delivery plan;pushing over the air updates to autonomous trucks to keep all componentsup to date; and other purposes or functions that improve the safety forthe autonomous vehicle, its cargo, and its surroundings. An oversightsystem may also determine performance parameters of an autonomousvehicle or autonomous truck, including any of: data logging frequency,compression rate, location, data type; communication prioritization; howfrequently to service the autonomous vehicle (e.g., how many milesbetween services); when to perform an MRC maneuver while monitoring thevehicle's progress during the maneuver; when to hand over control of theautonomous vehicle to a human driver (e.g., at a destination yard);ensuring an autonomous vehicle passes pre-trip inspection; ensuring anautonomous vehicle performs or conforms to legal requirements atcheckpoints and weight stations; ensuring an autonomous vehicle performsor conforms to instructions from a human at the site of a roadblock,cross-walk, intersection, construction, or accident; and the like.

Included in some of the functions executed by an oversight system orcommand center is the ability to relay over-the-air, real-time weatherupdates to autonomous vehicles in a monitored fleet. The over-the-airweather updates may be pushed to all autonomous vehicles in the fleet ormay be pushed to autonomous vehicles currently on a mission to deliver acargo. Alternatively, or additionally, priority to push or transmitover-the-air weather reports may be given to fleet vehicles currently ona trajectory or route that leads towards or within a predeterminedradius of a severe weather event.

Another function that may be encompassed by the functions executed by anoversight system or command center is the transmission of trailermetadata to the autonomous vehicle's computing unit (VCU) prior to thestart of a cargo transport mission. The trailer metadata may include thetype of cargo being transmitted, the weight of the cargo, temperaturethresholds for the cargo (e.g., trailer interior temperature should notfall below or rise above predetermined temperatures),time-sensitivities, acceleration/deceleration sensitivities (e.g.,jerking motion may be bad because of the fragility of the cargo),trailer weight distribution along the length of the trailer, cargopacking or stacking within the trailer, and the like.

To allow for communication between autonomous vehicles in a fleet and anoversight system or command center, each autonomous vehicle may beequipped with a communication gateway. The communication gateway mayhave the ability to do any of the following: allow for AV to oversightsystem communication (i.e. V2C) and the oversight system to AVcommunication (C2V); allow for AV to AV communication within the fleet(V2V); transmit the availability or status of the communication gateway;acknowledge received communications; ensure security around remotecommands between the AV and the oversight system; convey the AV'slocation reliably at set time intervals; enable the oversight system toping the AV for location and vehicle health status; allow for streamingof various sensor data directly to the command or oversight system;allow for automated alerts between the AV and oversight system; complyto ISO 21434 standards; and the like.

An oversight system or command center may be operated by one or morehuman, also known as an operator or an RCO. The operator may setthresholds for autonomous vehicle health parameters, so that when anautonomous vehicle meets or exceeds the threshold, precautionary actionmay be taken. Examples of vehicle health parameters for which thresholdsmay be established by an operator may include any of: fuel levels; oillevels; miles traveled since last maintenance; low tire-pressuredetected; cleaning fluid levels; brake fluid levels; responsiveness ofsteering and braking subsystems; Diesel exhaust fluid (DEF) level;communication ability (e.g., lack of responsiveness); positioningsensors ability (e.g., GPS, IMU malfunction); impact detection (e.g.,vehicle collision); perception sensor ability (e.g., camera, LIDAR,radar, microphone array malfunction); computing resources ability (e.g.,VCU or ECU malfunction or lack of responsiveness, temperatureabnormalities in computing units); angle between a tractor and trailerin a towing situation (e.g., tractor-trailer, 18-wheeler, orsemi-truck); unauthorized access by a living entity (e.g., a person oran animal) to the interior of an autonomous truck; and the like. Theprecautionary action may include execution of an MRC maneuver, seekingservice, or exiting a highway or other such re-routing that may be lesstaxing on the autonomous vehicle. An autonomous vehicle whose systemhealth data meets or exceeds a threshold set at the oversight system orby the operator may receive instructions that are automatically sentfrom the oversight system to perform the precautionary action.

The operator may be made aware of situations affecting one or moreautonomous vehicles in communication with or being monitored by theoversight system that the affected autonomous vehicle(s) may not beaware of. Such situations may include: irregular or sudden changes intraffic flow (e.g., traffic jam or accident); abrupt weather changes;abrupt changes in visibility; emergency conditions (e.g., fire,sinkhole, bridge failure); power outage affecting signal lights;unexpected road work; large or ambiguous road debris (e.g., objectunidentifiable by the autonomous vehicle); law enforcement activity onthe roadway (e.g., car chase or road clearing activity); and the like.These types of situations that may not be detectable by an autonomousvehicle may be brought to the attention of the oversight system operatorthrough traffic reports, law enforcement communications, data from othervehicles that are in communication with the oversight system, reportsfrom drivers of other vehicles in the area, and similar distributedinformation venues. An autonomous vehicle may not be able to detect suchsituations because of limitations of sensor systems or lack of access tothe information distribution means (e.g., no direct communication withweather agency). An operator at the oversight system may push suchinformation to affected autonomous vehicles that are in communicationwith the oversight system. The affected autonomous vehicles may proceedto alter their route, trajectory, or speed in response to theinformation pushed from the oversight system. In some instances, theinformation received by the oversight system may trigger a thresholdcondition indicating that MRC (minimal risk condition) maneuvers arewarranted; alternatively, or additionally, an operator may evaluate asituation and determine that an affected autonomous vehicle shouldperform an MRC maneuver and subsequently send such instructions to theaffected vehicle. In these cases, each autonomous vehicle receivingeither information or instructions from the oversight system or theoversight system operator uses its on-board computing unit (i.e., VCU)to determine how to safely proceed, including performing an MRC maneuverthat includes pulling-over or stopping.

Other interactions that the RCO may have with an autonomous vehicle or afleet of autonomous vehicle includes any of the following: pre-plannedevent avoidance; real-time route information updates; real-time routefeedback; trail hookup status; first responder communication requesthandling; notification of aggressive surrounding vehicle(s);identification of construction zone changes; status of an AV withrespect to its operational design domain (ODD), such as alerting the RCOwhen an autonomous vehicle is close to or enters a status out of ODD;RCO notification of when an AV is within a threshold distance from atoll booth and appropriate instruction/communication with the AV or tollauthority may be sent to allow the AV to bypass the toll; RCOnotification of when an AV bypasses a toll; RCO notification of when anAV is within a threshold distance from a weigh station and appropriateinstruction/communication with the AV or appropriate authority may besent to allow the AV to bypass the weigh station; RCO notification ofwhen an AV bypasses a weigh station; notification to the AV from the RCOregarding scheduling or the need for fueling or maintenance; RCOauthorization of third-party access to an autonomous vehicle cab;ability of an RCO to start/restart an autonomous driving system (ADS) ona vehicle; ability of an administrator (possibly an RCO) to set rolesfor system users, including ground crew, law enforcement, and thirdparties (e.g., customers, owners of the cargo); support from a RCO forcommunication with a service maintenance system with fleet vehicles;notification to the RCO from an AV of acceleration events; instructionfrom a RCO to an AV to continue its mission even when communication isinterrupted; RCO monitoring of an AV during and after an MRC maneuver isexecuted; support for continuous communication between an AV and a yardoperator at facility where the AV is preparing to begin a mission orwhere the AV is expected to arrive; oversight system monitoring ofsoftware systems on an AV and oversight system receiving alerts whensoftware systems are compromised; and the like.

An oversight system or command center may allow a third party tointeract with the oversight system operator, with an autonomous truck,or with both the human system operator and an autonomous truck. A thirdparty may be a customer whose goods are being transported, a lawenforcement or emergency services provider, or a person assisting theautonomous truck when service is needed. In its interaction with a thirdparty, the oversight system may recognize different levels of access,such that a customer concerned about the timing or progress of ashipment may only be allowed to view status updates for an autonomoustruck, or may be able to view status and provide input regarding whatparameters to prioritize (e.g., speed, economy, maintaining originallyplanned route) to the oversight system. By providing input regardingparameter prioritization to the oversight system, a customer caninfluence the route and/or operating parameters of the autonomous truck.

Features of an Autonomous Driving System in an Autonomous Truck

Actions that an autonomous vehicle, particularly an autonomous truck, asdescribed herein may be configured to execute to safely traverse acourse while abiding by the applicable rules, laws, and regulations mayinclude those actions successfully accomplished by an autonomous truckdriven by a human. These actions, or maneuvers, may be described asfeatures of the truck, in that these actions may be executableinstructions stored on the VCU 150 (i.e., the in-vehicle controlcomputer unit). These actions or features may include those related toreactions to the detection of certain types of conditions or objectssuch as: behaving appropriately when encountering emergency lanevehicles on the highway; interacting with stopped vehicles on theroadway; perform evasive maneuvers in emergency situations; classify,track, and autonomously respond to motorcycles; be able to handle alllevels of traffic autonomously; be able to detect and analyze objectswithin its field of view (FOV); maintain position within a given lane oftravel; control, optimize, and maintain vehicle speed autonomously;properly identify and respond to both active and inactive railwaycrossings; interpret law enforcement behaviors and properly respond anddefer to an on-board operator or yard operator, if needed; autonomouslynavigate accident areas; autonomously respond to roadway crashes andaccidents; identify and autonomously interact with school buses; detectand respond to animals while causing the least harm; navigate a parkinglot and park; identify and navigate low vertical clearance situations;detect when out-of-ODD and execute the proper MRC; operate within zoneswith constant definition (e.g., fixed zones, school zone); dynamicdriving tasks; detect and respond to oncoming traffic; navigate weighstations; navigate through tunnels; classify roadway traction properlyrespond; identify and operate in dynamic zones (e.g., zones withvariable rules for time of day or day of the week); navigate toll roador toll booths; identify and respond to traffic signs; detect roadwaygrades (incline) for operational limits as defined in ODD (operationaldesign domain); detect and track roadway superelevation for operationallimits as defined in ODD; navigate areas with reduced localizationsensor reliability; operate within unmapped construction zones; classifyand navigate road blockages; operate within mapped construction zones;detect and navigate a service station; collect and transmit informationabout roadway misuse; navigate roadways with restricted lanes; obeyhuman traffic controller; properly operate on roadways that are notentirely covered with water when road friction is sufficient foroperation; detect icy roads and classify the severity of icing; navigateintersections; navigate freeway/highway interchanges; continuedoperation during periods of low wireless communications; operations inall lighting conditions; operation in all roadway types defined in theoperational design domain; utilize road should appropriately; monitorother vehicles behavior for possible hijacking; respond appropriately totraffic lights; operate in inclement weather; and the like.

Other features of an autonomous truck may include those actions orfeatures which are needed for any type of maneuvering, including thatneeded to accomplish the features or actions that are reactionary,listed above. Such features, which may be considered supportingfeatures, may include: the ability to navigate roundabouts; the abilityto properly illuminate with on-vehicle lights as-needed for ambientlight and for compliance with local laws; apply the minimum amount ofdeceleration needed for any given action; determine location at alltimes; adapting dynamic vehicle control for trailer load distributions,excluding wheel adjustment; launching (reaching target speed),accelerating, stopping, and yielding; operate on roadways with bumps andpotholes; enter an MRC on roadway shoulders; access local laws andregulations based on location along a route; operate on asphalt,concrete, mixed grading, scraped road, and gravel; ability to operate inresponse to metering lights/signals at on-ramps; operate on a roadwaywith a width up to a predetermined width; able to stop at crosswalkswith sufficient stopping distance; navigate two-way left turn lanes;operate on roadways with entry and exit ramps; utilize the vehicle hornto communicate with other drivers; and the like. These supportingfeatures, as well as the reactionary features listed above, may includecontrolling or altering the steering, engine power output, brakes, orother vehicle control subsystems 146.

Situational Behavior

The autonomous vehicle 105 can be configured to adjust its actions ormaneuvers based on the detection of various different situations thatthe autonomous vehicle 105 may encounter. For example, the autonomousvehicle 105 can be configured to, under normal or optimal conditions,operate according to a default decision framework. Upon detecting aspecific set of conditions that are consistent with a define situation,the autonomous vehicle 105 can be configured to alter the defaultdecision framework in order to take certain actions that can improvesafety for both the autonomous vehicle 105 and other entities on or nearthe roadway. Thus, it is desirable for the autonomous vehicle 105 to beable to identify certain predefined situations and modify the autonomousvehicle's 105 behavior in response to detecting one of the predefinedsituations in order to safely manage the detected situation.

There are a number of aspects related to the situational behavior of theautonomous vehicle's 105, including crash mitigation strategy (CMS),evasive maneuvers, autonomous dynamic driving tasks, low communicationreception, automated horn, non-compliant vehicle road user detection,scout monitoring, and oncoming traffic detection, among others.

The in-vehicle control computer 150 of the autonomous vehicle 105described herein can address at least some of the above describedproblems by determining that another vehicle is violating one or morerules of the roadway based on perception data received from one or moresensors of an autonomous vehicle, tagging the other vehicle as anon-compliant driver, and modifying control of the autonomous vehicle inresponse to tagging the other vehicle as a non-compliant driver.

Crash Mitigation Strategy (CMS)

One important aspect involved in safely navigating an autonomous vehicle105 is the detection and mitigation of crashes or other accidents. Thus,the in-vehicle control computer 150 can be configured to detect when theautonomous vehicle 105 has been involved in an accident, determine theseverity of the accident, and based on the detection and severity,determine a course of action to minimize any further damage to theautonomous vehicle 105 and any other entities on or near the roadway.

As used herein, a car accident, also referred to as a “trafficcollision,” or a “motor vehicle accident,” generally refers to thesituation in which a motor vehicle strikes or collides another vehicle,a stationary object, a pedestrian, and/or an animal. While some caraccidents result only in property damage, others may result in severeinjuries or death.

The in-vehicle control computer 150 can be configured to detect a caraccident in which the autonomous vehicle 105 is involved. For example,the in-vehicle control computer 150 can be configured to detect such anaccident based on the information received from inertial sensors (e.g.,when in a crash with one or more other vehicles), cameras, and/or lidars(e.g., when in a crash with vulnerable road users or animals).

In response to detecting that the autonomous vehicle 105 being involvedin a car accident, the in-vehicle control computer 150 can be configuredto determine the severity of the car accident. For example, thein-vehicle control computer 150 can be configured to determine that theseverity of a car accident is severe in response to a detecteddeceleration being greater than a threshold deceleration and/orrecognizing that an object the autonomous vehicle 105 has collided withis a pedestrian, cyclist, motorcycle, or other vulnerable road user(VRU). The threshold deceleration may be, for example, 4G, 5G, or 6G,however, other thresholds values are also possible without departingfrom aspects of this disclosure.

As used herein, a vulnerable road user may generally refer to a roaduser without the protection of an outside shield. Example VRUs include:pedestrians, bicyclists, skateboarder, rollerbladers, and skaters.

The in-vehicle control computer 150 can also be configured to determinethat the severity of a car accident is light in response to a detecteddeceleration being less than the threshold deceleration and recognizingthat any object the autonomous vehicle 105 has collided with is not apedestrian, cyclist, and/or motorcycle.

The in-vehicle control computer 150 can also be configured to controlthe autonomous vehicle 105 to make a complete stop in response todetecting that the autonomous vehicle 105 has been involved in anaccident while the autonomous vehicle 105 is moving.

The in-vehicle control computer 150 can also be configured to turn onthe autonomous vehicle's 105 hazard lights in response to detecting thatthe autonomous vehicle 105 has been involved in an accident.

In response to detecting that the autonomous vehicle 105 being involvedin an accident, the in-vehicle control computer 150 can be configured toperform a diagnostic procedure. The in-vehicle control computer 150 canalso be configured to contact the oversight system 350 in response todetecting that the autonomous vehicle 105 being involved in an accident,and in response to a request from an operator 355 at the oversightsystem 350, the in-vehicle control computer 150 can be configured totransmit the results of the diagnostic procedure to the operator 355. Insome implementations, the diagnostic procedure can identify whethervehicle critical systems are still functioning (in particular, thevehicle critical systems involved in performing the first MRC maneuver).

In some embodiments, the in-vehicle control computer 150 can furtherdetermine that severity is light in response to determining that one ormore of the following conditions is satisfied: there is no body damageto the autonomous vehicle 105, the engine is still running, thediagnostic results do not show any malfunction of autonomous vehicle105, and/or there are no debris and/or obstacles which will restrictmovement of the autonomous vehicle 105. The in-vehicle control computer150 can be configured to control the autonomous vehicle 105 to executethe first MRC maneuver in response to determining that the severity ofthe accident is light.

The in-vehicle control computer 150 can also be configured to wait for aresponse from the operator 355 in response to determining that theseverity of the accident is severe. The in-vehicle control computer 150can control the autonomous vehicle 105 to execute the first MRC maneuverin response to receiving an instruction from the operator 355 to executethe first MRC maneuver and if the diagnostic procedures indicate thatthe vehicle critical systems involved in performing the first MRCmaneuver are in condition to execute the first MRC maneuver.

In some embodiments, the in-vehicle control computer 150 can also beconfigured to remain stationary until the first MRC maneuver istriggered by the operator 355 (e.g., in the case that the in-vehiclecontrol computer 150 has detected a severe crash) or the in-vehiclecontrol computer 150 has determined to execute the first MRC maneuver(e.g., in the case of a light crash).

Evasive Maneuvers

Another aspect involved in safely navigating an autonomous vehicle 105is the performance of evasive maneuvers to avoid or mitigate anyaccidents. The in-vehicle control computer 150 can be configured todetermine whether there is a potential for any accidents and whether anaccident is imminent, and determine a course of action to avoid orminimize any damage to the autonomous vehicle 105 and any other entitieson or near the roadway. The in-vehicle control computer 150 can beconfigured to avoid colliding with any road users or obstacles wheneverpossible to ensure the safety of all road users.

As used herein, an evasive maneuver generally refers to a maneuver takenby the autonomous vehicle 105 to avoid or mitigate the impact of apotential or imminent accident. For example, evasive maneuvers caninclude swerving, braking, or a combination thereof.

As used herein, swerving generally refers to an input to the autonomousvehicle's 105 steering to change the heading of the autonomous vehicle105 that does not cause understeer or tipping of the trailer. As usedherein, evasive steering may generally refer to an input to theautonomous vehicle's 105 steering to change the heading of theautonomous vehicle 105 that does not cause the autonomous vehicle 105 toskid or the trailer to tip.

As used herein, braking generally refers to an input to the autonomousvehicle's 105 braking to reduce the speed of the autonomous vehicle 105based on the amount of speed reduction determined to execute a desiredaction up to the maximum braking capability of the autonomous vehicle105. As used herein, evasive braking may generally refer to an input tothe autonomous vehicle's 105 braking to reduce the speed of theautonomous vehicle 105 using critical deceleration up to the maximumdeceleration limit of the autonomous vehicle 105.

In response to executing an evasive maneuver, the in-vehicle controlcomputer 150 can be configured to activate the autonomous vehicle's 105hazard lights to inform other entities on or near the roadway that theautonomous vehicle 105 is performing evasive maneuvers in response tothe potential or imminent accident.

The in-vehicle control computer 150 can be configured to refrain fromexecuting evasive maneuvers except in emergency scenarios. As usedherein, an emergency scenario may generally refer to a scenario which anaccident is considered to be imminent. For example, the in-vehiclecontrol computer 150 can be configured to determine that a scenario isconsidered to be imminent when the autonomous vehicle 105 is on a pathto collide with another entity within a predicted time to collision(TTC) that is less than a threshold time period. The threshold timeperiod may, for example, include 0.75 s, 1 s, 1.25 s, 1.5 s, 1.75 s, 2s, however, other threshold time periods are also possible withoutdeparting from aspects of this disclosure.

In some embodiments, the in-vehicle control computer 150 can beconfigured to determine that a potential collision is considered to beimminent when the in-vehicle control computer 150 predicts/calculatesthat the critical distance between the autonomous vehicle 105 and a roaduser cannot be maintained even using the upper limits of harshdeceleration. As used herein, harsh deceleration may correspond to apredetermined range of deceleration, for example, ranging from 2-4 m/s2.In some embodiments, the in-vehicle control computer 150 can predict thecritical distance based on the trajectory of the autonomous vehicle 105as well as the other road user and determine whether the criticaldistance between the autonomous vehicle 105 and the other road user willbe broken.

In general, the in-vehicle control computer 150 can be configured tocause the autonomous vehicle 105 to stay within its current lane whenexecuting an evasive maneuver unless evasive braking alone is not enoughto prevent collision. This can help reduce the possibility of additionalliability and risk that may accompany an evasive steering. For example,as used herein the autonomous vehicle 105 can execute evasive maneuverswith braking or with a combination of braking and swerving. In the casethat the autonomous vehicle 105 can perform an evasive maneuver withoutswerving (e.g., by braking without leaving the current lane), theautonomous vehicle 105 can be configured to do so since performing anevasive maneuver without swerving may involve less risk than moving intoan adjacent lane with little to no notice or signaling.

The in-vehicle control computer 150 can be configured to detect a fastreveal scenario. As used herein, a fast reveal scenario may generallyrefer to a scenario in which an occluded or obstructed entity or staticobject 422 in the autonomous vehicle's 105 lane is suddenly revealed orotherwise becomes detectable by the autonomous vehicle 105 due to thesource of occlusion 424 being suddenly removed. FIG. 4A illustrates anexample visualization of a fast reveal scenario in accordance withaspects of this disclosure.

In response to detecting a fast reveal scenario, the in-vehicle controlcomputer 150 can be configured to cause the autonomous vehicle 105 tobrake up to the maximum amount of braking within safety limits to ensurethe critical distance between the autonomous vehicle 105 and a target422 of the fast reveal scenario can be maintained while in lane. As usedherein, safety limits may generally refer to a maximum brakingcapability that the autonomous vehicle 105 can handle, which may varybased on a load in a trailer of the autonomous vehicle and/orenvironmental road conditions. The vehicle in-vehicle control computer150 can be configured to determine the autonomous vehicle's 105 brakinglimitations and can be configured to avoid exceeding the limits in orderto prevent a potential safety critical incident. If the in-vehiclecontrol computer 150 determines that the critical distance cannot bemaintained while the autonomous vehicle 105 is braking in lane, thein-vehicle control computer 150 can be configured to cause theautonomous vehicle 105 to continue braking and swerve into an escapespace if the escape space is available.

As used herein, a critical distance may generally refer to apredetermined distance from any other entity or static obstacle at alltimes from the closest point of the autonomous vehicle 105 to theclosest point of the other entity or static obstacle. The predetermineddistance may include, for example, 0.25 m, 0.5 m, 0.75 m, however, otherdistances are also possible. The in-vehicle control computer 150 can beconfigured to maintain the critical distance during all maneuversincluding evasive maneuvers. Advantageously, but maintaining a criticaldistance from all other entities and static obstacle, the in-vehiclecontrol computer 150 can provide a buffer that improves the safety forall road users.

As used herein, an escape space (also referred to as evasive free space)may generally refer to a space adjacent to the autonomous vehicle 105within an expanded drivable area that is large enough for the autonomousvehicle 105 to swerve into when executing an evasive maneuver. In otherwords, evasive free space may generally refer to areas adjacent to theautonomous vehicle 105 within the carriageway that is large enough forthe autonomous vehicle to swerve into when executing an evasivemaneuver. The in-vehicle control computer 150 can be configured todetermine that an escape space is available if the escape space is clearof any entities within a predetermined length of time. The predeterminedlength of time may include, for example, 2 s, 3 s, 4 s, 5 s, however,other lengths of time are also possible without departing from aspectsof this disclosure.

As used herein, an expanded drivable area may generally refer to allroad surfaces that are not in the opposite lane and without hardboundaries that can be used when conducting an evasive maneuver.

In some embodiments, the in-vehicle control computer 150 can also beconfigured to detect a sudden braking scenario. As used herein, a suddenbraking scenario may generally refer to a scenario in which the entityin front of the autonomous vehicle 105 and in the same lane brakessuddenly with a deceleration of more than a predetermined amount ofdeceleration. The predetermined amount of deceleration can include, forexample, 2.5 m/s2, 3 m/s2, 4 m/s2, 5 m/s2, 6 m/s2, although otherdeceleration amounts are also possible. FIGS. 4B-4C illustrate examplevisualizations of sudden braking scenarios in accordance with aspects ofthis disclosure.

In response to detecting a sudden braking scenario, the in-vehiclecontrol computer 150 can be configured to cause the autonomous vehicle105 to brake up to the maximum amount of braking within safety limits toensure the critical distance between the autonomous vehicle 105 and thetarget of the sudden braking scenario can be maintained while in lane.If the in-vehicle control computer 150 determines that the criticaldistance cannot be maintained from another vehicle 426 or entity whilethe autonomous vehicle 105 is braking in lane, the in-vehicle controlcomputer 150 can be configured to cause the autonomous vehicle 105 tocontinue braking and swerve into an escape space if the escape space isavailable.

Another scenario which can be detected by the in-vehicle controlcomputer 150 is a lateral intrusion scenario. As used herein, a lateralintrusion scenario may generally refer to a scenario in which an entity426 that is travelling in a parallel lane or direction is rapidlyintruding into the autonomous vehicle's 105 lane and the in-vehiclecontrol computer 150 predicts that the entity 426 will collide with aside of the autonomous vehicle 105. FIG. 4D illustrates an examplevisualization of a lateral intrusion scenario in accordance with aspectsof this disclosure.

In response to detecting a lateral intrusion scenario, the in-vehiclecontrol computer 150 can be configured to cause the autonomous vehicle105 to swerve if an escape space on the opposite side of the potentialcollision side is available to maintain the critical distance from theentity 426. After swerving, the in-vehicle control computer 150 can beconfigured to cause the autonomous vehicle 105 to slow down to avoidstaying parallel to the intruding vehicle. If an escape route is notavailable, the in-vehicle control computer 150 can be configured tocause the autonomous vehicle 105 to only use evasive braking and slowthe autonomous vehicle 105 down to avoid the intruding vehicle.

The in-vehicle control computer 150 can also be configured to detect across path scenario. As used herein, a cross path scenario may generallyrefer to a scenario in which an entity 426 travelling in a non-parallellane or direction is predicted to cross with the autonomous vehicle's105 path. FIG. 4E illustrates an example visualization of a cross pathscenario in accordance with aspects of this disclosure.

In response to detecting a cross path scenario, the in-vehicle controlcomputer 150 can be configured to cause the autonomous vehicle 105 tobrake up to the maximum amount of braking within safety limits to ensurethe critical distance between the autonomous vehicle 105 and the target426 of the cross path scenario can be maintained while in lane. If thein-vehicle control computer 150 determines that the critical distancecannot be maintained while the autonomous vehicle 105 is braking inlane, the in-vehicle control computer 150 can be configured to cause theautonomous vehicle 105 to continue braking and swerve into an escapespace if the escape space is available.

Another scenario which can be detected by the in-vehicle controlcomputer 150 is an oncoming scenario. As used herein, an oncomingscenario may generally refer to a scenario in which an entity 426travelling in a parallel lane but in an opposite direction to theautonomous vehicle 105 is predicted to intersect with the autonomousvehicle's 105 path. FIG. 4F illustrates an example visualization of anoncoming scenario in accordance with aspects of this disclosure.

In response to detecting an oncoming scenario, the in-vehicle controlcomputer 150 can be configured to cause the autonomous vehicle 105 tobrake up to the maximum amount of braking within safety limits andswerve away from the oncoming entity 426. In some embodiment, theautonomous vehicle 105 can swerve into the escape space (also referredto as an “out”) if the escape space is available. An escape space caninclude unoccupied areas adjacent to the autonomous vehicle 105including in front, behind, or to either side of the autonomous vehicle105 or a combination thereof. The in-vehicle control computer 150 canperiodically (e.g., continuously in some embodiment) determine whetherthe escape spaces are available for the autonomous vehicle 105 to moveinto. If the escape space is unavailable, the in-vehicle controlcomputer 150 can be configured to cause the autonomous vehicle 105 touse only evasive braking.

The in-vehicle control computer 150 can further be configured to detecta cut-in scenario. As used herein, a cut-in scenario may generally referto a scenario in which an entity 426 travelling in a parallel lane ordirection as the autonomous vehicle 105 is predicted to cut into theautonomous vehicle's 105 path. FIG. 4G illustrates an examplevisualization of a cut-in scenario in accordance with aspects of thisdisclosure.

In response to detecting a cut-in scenario, the in-vehicle controlcomputer 150 can be configured to cause the autonomous vehicle 105 tobrake up to the maximum amount of braking within safety limits to ensurethe critical distance between the autonomous vehicle 105 and the target426 of the cut-in scenario can be maintained while in lane. If thecritical distance cannot be maintained while the autonomous vehicle 105is braking in lane, the autonomous vehicle 105 may continue braking andswerve away from the direction of the cut in and direct the autonomousvehicle 105 into the escape space if the escape space is available. Ifthe escape space is unavailable, the in-vehicle control computer 150 canbe configured to cause the autonomous vehicle 105 to use only evasivebraking.

In some embodiments, the in-vehicle control computer 150 can also beconfigured to predict whether an emergency scenario has a probabilitythat is greater than a threshold probability based on perception datareceived from one or more of the sensors of the vehicle sensorsubsystems 144. In particular, the in-vehicle control computer 150 canuse perception data related to entities in the same lane in front of theautonomous vehicle 105 as well as entities in adjacent lanes andentities in cross paths when the autonomous vehicle 105 is at or near anintersection.

In some implementations, when considering whether to perform any type ofevasive maneuver, the in-vehicle control computer 150 can consider theranges of possible motion (e.g., predicted motion) for the types ofentities in the immediate surroundings of the autonomous vehicle 105.For example, if an entity is predicted to enter an escape space, thein-vehicle control computer 150 can determine that the safe space is notavailable for an evasive maneuver.

The in-vehicle control computer 150 can also be configured to select andmonitor the closest entities (e.g., entities within a predetermineddistance) within the autonomous vehicle's 105 predicted path of travelas well as entities with a predicted path that crosses the autonomousvehicle's 105 predicted path as potential targets for evasive maneuvers.Any monitored entity that meets the criteria for performing an evasivemaneuver in response to a predefined scenario may trigger the in-vehiclecontrol computer 150 to take actions consistent with the determinedscenario.

When the in-vehicle control computer 150 has determined to perform anevasive maneuver, the in-vehicle control computer 150 can commit thetarget of the evasive maneuver to memory until the autonomous vehicle105 has completely come to a stop and/or if the emergency scenario is nolonger valid.

After determining that the autonomous vehicle 105 has executed anevasive maneuver and has successfully avoided an accident, thein-vehicle control computer 150 can be configured to return theautonomous vehicle 105 to normal operations and the autonomous vehicle's105 previous lane of travel prior to the evasive maneuver provided theintended lane and trajectory is clear of other entities and/orobstacles.

The in-vehicle control computer 150 can be configured to determine ifthe autonomous vehicle 105 is in an accident and a collision hasoccurred. For example, the in-vehicle control computer 150 can use themotion of the autonomous vehicle 105 and the distance of the autonomousvehicle 105 to other entities to determine if the autonomous vehicle 105is in an accident. After the in-vehicle control computer 150 hasdetermined that the autonomous vehicle 105 is in an accident and theautonomous vehicle 105 has come to a stop, the in-vehicle controlcomputer 150 can be configured to continue holding the brakes for anextra define length of time (e.g., 5 s) to prevent the possibility ofthe autonomous vehicle 105 being displaced due to secondary impact(s).

Autonomous Dynamic Driving Tasks

The in-vehicle control computer 150 can be configured to performreal-time operational and tactical functions which can be termed dynamicdriving tasks. As used herein, dynamic driving tasks (DDT) may generallyrefer to all of the real-time operational and tactical functionsrequired to operate an autonomous vehicle 105 in on-road traffic. DDTmay differ from driving because DDT can include tactile and functionaleffort, but can exclude strategic effort. DDT may not even involve theautonomous vehicle 105 being in motion.

As used herein, object and event detection and response (OEDR) maygenerally refer to the subtasks of the DDT that include monitoring thedriving environment (detecting, recognizing, and classifying objects andevents and preparing to respond as needed) and executing an appropriateresponse to such objects and events.

The in-vehicle control computer 150 can be configured to detect andclassify all on-road and off-road objects, as well as the environmentalevents that impact the driving tasks. The in-vehicle control computer150 can be configured to detect system failures that impact the abilityto complete driving tasks.

The in-vehicle control computer 150 can be configured to detect allon-road stationary and dynamic objects that impact the driving tasks.For example, the in-vehicle control computer 150 can be configured todetect the following static on-road objects: road and lane markings,construction signs, obstructions, and other on-road static objects. Thein-vehicle control computer 150 can also be configured to detect thefollowing dynamic on-road objects: 4 wheelers vehicles, 2 wheelers(e.g., cyclists, motorcycles), pedestrians, dynamic traffic signs, andother on-road dynamic objects (e.g., animals, unknown objects).

The in-vehicle control computer 150 can be configured to detect alloff-road stationary and dynamic objects that impact the driving tasks.For example, the in-vehicle control computer 150 can be configured todetect the following static off-road objects: road and traffic signs,traffic lights, curbs, construction signs, vehicles (parked/stopped),pedestrians, and other off-road static objects. The in-vehicle controlcomputer 150 can be configured to detect the following dynamic off-roadobjects: vehicles (stopping in curbside or merging into the road),pedestrians, and other off-road dynamic objects.

The in-vehicle control computer 150 can be configured to detectenvironmental events in order to respond to them autonomously andsafely. For example, environmental events can include weather conditionsand road conditions (e.g., accident areas).

The in-vehicle control computer 150 can be configured to adjust theautonomous vehicle's 105 lateral position by adjusting the steeringangle. The in-vehicle control computer 150 can be configured to followthe limitations of steering (e.g., maximum turning angle, maximum turnrate). Different maneuvers may translate to a set of lateral controltasks.

The in-vehicle control computer 150 can be configured to adjust theautonomous vehicle's 105 longitudinal position by accelerating anddecelerating to a speed that is required by DDT. The in-vehicle controlcomputer 150 can be configured to accelerate the autonomous vehicle 105to a desired speed safely with a predetermined maximum rate. Thein-vehicle control computer 150 can be configured to decelerate theautonomous vehicle 105 to a desired speed safely with a predeterminedmaximum rate. The in-vehicle control computer 150 can be configured tocontrol the autonomous vehicle 105 to complete a desired STOP maneuver(e.g., comfort stop, safe stop, emergency stop). Different maneuvers maytranslate to a set of longitudinal control tasks.

The in-vehicle control computer 150 can be configured to plan maneuversthat are required to navigate autonomously and safely in ODD. Thein-vehicle control computer 150 can be configured to complete DDT thatare required for all the plan maneuvers.

Example operational and tactical maneuvers can include: parking,maintaining speed, target car following, lane centering, lane changing,passing (including aborting passing), enhancing conspicuity, obstacleavoidance, low-speed merge, high-speed merge, navigating on/off ramps,yielding (e.g., right-of-way decisions), following driving laws,navigating roundabouts, navigating intersections, navigating crosswalks,navigating work zones, turning, U-Turns, transitioning to MRC maneuvers,and route planning.

Low Communication Reception

Since the autonomous vehicle 105 may communicate with the oversightsystem 350 during navigation, it can be beneficial to determine thereliability of the autonomous vehicle's 105 wireless connection. As usedherein, wireless communication reliability may generally refer to thereception rate of the autonomous vehicle's 105 wireless transceiver(e.g., the network communications subsystem 178). A low reception ratemay be the general case defined by a packet drop ratio that is higherthan a threshold value.

A reliability unit can be measured using a metric in the interval of[0,1]. Reliability units can include a probabilistic model describingthe behavior of losing communication. In this way, the in-vehiclecontrol computer 150 can predict the loss of communication. The metriccan be configured to measure quantitative characteristics or variables.

The in-vehicle control computer 150 can be configured to determine thereliability of the wireless communication using the reception rateexperienced while driving during the last predetermined distance. Thepredetermined distance can include, for example, 400 m, 500 m, 600 m,although other distances are also possible. The in-vehicle controlcomputer 150 can determine the reliability of the wireless communicationbased on the average of reception rate.

The in-vehicle control computer 150 can detect the regaining of wirelesscommunication using the reliability of wireless communication thresholdof a predetermined number of reliability units. In some embodiments, thepredetermined number of reliability units can include, for example, 0.3,0.4, or 0.5 reliability units, although other values are possiblewithout departing from aspects of this disclosure.

In response to determining that the reliability of wirelesscommunication is less than a threshold value, the in-vehicle controlcomputer 150 can continue to drive on the current route using an onboardlocalization system for a predetermined distance. If the in-vehiclecontrol computer 150 does not regain wireless communication within thepredetermined distance, the in-vehicle control computer 150 can activatethe first MRC maneuver. In some embodiments, the threshold value caninclude, for example, 0.5, 0.1, 0.2 reliability units and thepredetermined distance can include, for example, 1 mile, 1.5 miles, 2miles, 2.5 miles, although other values are also possible. Thereliability of wireless communication can be normalized in the intervalof [0,1] reliability units.

In response to determining that the reliability of wirelesscommunication is lower than a threshold value, the in-vehicle controlcomputer 150 can prioritize data to be sent to the oversight system 350and continue the current mission to the destination. The threshold valuecan be, for example, 0.5, 0.6, 0.7, 0.8 reliability units, althoughother values are also possible.

In response to determining that the reliability of wirelesscommunication is lower than a threshold value, the in-vehicle controlcomputer 150 can reduce the update rate of data to be sent to theoversight system 350 and continue the current mission to thedestination. The threshold value can be, for example, 0.3, 0.4, 0.5reliability units, although other values are also possible. When thein-vehicle control computer 150 reduces data sent to the oversightsystem 350, communication can be established when the reliability ofwireless communication is lower than a threshold value.

The in-vehicle control computer 150 can also be configured to determinewhether the map indicates that a tunnel is on the current route of theautonomous vehicle 105 within a predetermined threshold distance aheadof the autonomous vehicle 105. In response to determining that a tunnelis within the predetermined threshold distance, the in-vehicle controlcomputer 150 can be configured to plan to rely more on the onboardlocalization system to ensure the autonomous vehicle 105 is moving inthe right direction and when the autonomous vehicle 105 comes out oftunnel, the in-vehicle control computer 150 can resume normal usage ofwireless communication for localization purposes. The predeterminedthreshold distance can include, for example, 75 m, in the next 100 m, inthe next 150 m, although other distances are also possible withoutdeparting from aspects of this disclosure. The in-vehicle controlcomputer 150 can be configured to avoid activating the first MRCmaneuver while in the tunnel and while there is no wirelesscommunication available.

The in-vehicle control computer 150 can also be configured to cause theautonomous vehicle 105 to continue the current mission using an onboardlocalization system in response to determining that the map indicatesthat the current route includes a road segment with low reception. Inresponse to detecting the regaining of communication, the in-vehiclecontrol computer 150 can inform the oversight system 350 about thecurrent location of the autonomous vehicle 105.

Automated Horn

The autonomous vehicle 105 can be equipped with a horn that can be usedto communicate with other entities on or near the roadway, particularlywhen such communication can be used to prevent or reduce the severity ofan accident. The autonomous vehicle 105 may use a number of differentinputs in determining whether to actuate the horn and what type of hornto activate.

The autonomous vehicle 105 can be configured to activate at least twodifferent types of horn activation: short and long. In someimplementations, a short horn may be defined as an activated horn for afirst predetermined period of time (e.g., 0.25 sec, 0.5 sec, 0.75 sec)and a long horn may be defined as an activated horn for a secondpredetermined period of time (e.g., 0.5 sec, 1 sec, 1.5 sec).

Prior to actuating the horn, the autonomous vehicle 105 may determinewhether the autonomous vehicle 105 is currently located in a zone wherehorn activation is prohibited. In some embodiments, the autonomousvehicle 105 can store a predetermined map which can include zones whereactivating the horn is prohibited. Thus, prior to activating the hornthe autonomous vehicle 105 can determine whether its current position isoutside of any zones where activating the horn is prohibited. Dependingon local regulations, horn activation may be prohibited on local streetsof residential areas between the hours of 11.30 pm and 7.00 am,hospitals, and/or in school zones. In some embodiments, the autonomousvehicle 105 can also determine whether the autonomous vehicle 105 islocated in a zone where horn use is prohibited using one of moresensors. For example, the autonomous vehicle 105 can determine that itis located in a school zone based on detecting a sign indicating thepresence of a school zone.

Another aspect in determining when to activate the horn of theautonomous vehicle 105 includes detecting other entities (e.g., NPCs) onor near the roadway. For example, the autonomous vehicle 105 can detectmoving entities and attributes associated with the detected entities ata predetermined distance away from the autonomous vehicle 105 (e.g., 100m, 150 m, 175 m, 200 m). The autonomous vehicle 105 can be configured todetect one or more of the following attributes associated with eachdetected moving entity: position, speed, direction of movement of themoving entity, acceleration, etc.

One situation in which the autonomous vehicle 105 can activate the longhorn is when the safety of a moving entity is at risk. For example, whenthe autonomous vehicle 105 detects that an oncoming vehicle has crossedthe lane divider and a lane bias level of the oncoming vehicle isinsufficient to reach a lateral gap greater than a predeterminedthreshold distance (e.g., 0.75 m, 1 m, 1.25 m, 1.5 m), the autonomousvehicle 105 may activate the long horn once. Other situations underwhich the autonomous vehicle 105 may activate the long horn in responseto detecting a potential risk to the safety of a moving entity and thelateral gap are described in the Oncoming Traffic Detection section.

One situation in which the autonomous vehicle 105 can activate the shorthorn is when certain types of stationary entities are detected. Forexample, when the autonomous vehicle 105 detects a vehicle (except foremergency vehicles and/or vehicles with hazard lights on) is stopped fora predetermined length of time (e.g., 8 sec, 10 sec, 12 sec, 15 sec)with no vehicle in front of the autonomous vehicle 105 and a lane changeis not possible, the autonomous vehicle 105 can activate the short hornonce. Certain local laws and/or regulations may prohibit sounding thehorn in response to a stationary entity unless necessary to avoid acollision. Thus, the autonomous vehicle 105 may only activate the shorthorn in response to detecting a stationary vehicle as described abovewhen the autonomous vehicle 105 determines that activating the horn canavoid a potential collision.

Another situation in which the autonomous vehicle 105 can activate thelong horn is when the autonomous vehicle 105 predicts that a reversingvehicle will collide with the autonomous vehicle 105 and the predictedTTC is less than or equal to a threshold amount of time (e.g., about 2sec, about 3 sec, about 4 sec, about 5 sec). The autonomous vehicle 105may activate the long horn once in response to the above conditionsbeing met.

Another situation in which the autonomous vehicle 105 can activate theshort horn is when a road corner/curve is detected and the currentvisibility is less than a predetermined distance (e.g., 150 feet, 200feet, 225 feet, 250 feet). When these conditions are met, the autonomousvehicle 105 can activate the short horn once before crossingcorner/curve to warn oncoming vehicles or other entities.

Yet another situation in which the autonomous vehicle 105 can activatethe short horn is when a “non-compliant driver-lane crossing vehicle”and/or a “non-compliant driver-too Close for Comfort” are detected. Theautonomous vehicle 105 may activate the short horn once when theseconditions are met.

Non-Compliant Driver Detection

One aspect to the safe autonomous navigation of an autonomous vehicle105 includes the detection and tracking of non-compliant drivers (alsoreferred to as non-compliant vehicle road users).

As used herein, a non-compliant driver may generally refer to a vehicleroad user that is not complying with one or more traffic rules. Thein-vehicle control computer 150 can be configured to detect, classify,and/or respond to non-compliant drivers based on the particular rule(s)being violated by the non-compliant driver. For example, the in-vehiclecontrol computer 150 can be configured to classify non-compliant driversinto one or more of the following classifications: lane crossing,erratic, speeding, oscillating, intersection, and/or static.

The in-vehicle control computer 150 can be configured to detect lanecrossing non-compliant drivers. As used herein, a lane crossingnon-compliant driver may generally refer to as a non-compliant driverwho illegally crosses a lane line into the autonomous vehicle's 105 laneor a lane adjacent to the autonomous vehicle's 105 lane. Lane crossingnon-compliant drivers may present the additional risk of becoming acritical distance cut in vehicle, which can be a safety hazard for theautonomous vehicle 105.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect a lane crossing non-compliant driver that crossesany lane boundary (including the mirrors of the non-compliant driver'svehicle), without changing lanes and/or without the usage of turnsignals, while driving in front of and in the same lane as theautonomous vehicle 105. The in-vehicle control computer 150 can beconfigured to detect a lane crossing non-compliant driver that crossesthe lane boundary (including mirrors of the non-compliant driver'svehicle) on the side closest to the autonomous vehicle 105 withoutchanging lanes while the non-compliant driver is driving in a laneadjacent to the autonomous vehicle's 105 lane-of-travel.

The in-vehicle control computer 150 can be configured to detect erraticnon-compliant drivers. As used herein, an erratic non-compliant drivermay generally refer to a non-compliant driver who breaks traffic laws ina variety of ways. Erratic non-compliant drivers may operate vehicles tomake aggressive maneuvers and the in-vehicle control computer 150 can beconfigured to avoid non-compliant vehicles as much as possible for thesafety of the autonomous vehicle 105 and other road users. For example,an erratic non-compliant driver may break traffic laws in one or more ofthe following ways: a vehicle that is driving the wrong way, opposite tothe flow of traffic, a vehicle that is driving outside of the drivablelanes or on a gore area, and/or a vehicle that cuts across multiplelanes of traffic without utilizing turn signal. As used herein, a gorearea generally refers to an area that is in the emergency lane area andis often found in between a lane split or highway exit for example.Please see the Map Taxonomy Section for additional details regardinggore areas.

The in-vehicle control computer 150 can also be configured to detectspeeding non-compliant drivers. As used herein, a speeding non-compliantdriver may generally refer to a driver who is driving significantlyfaster than the speed limit of the roadway the vehicle is driving on. Insome embodiments, the in-vehicle control computer 150 can label avehicle as a speeding non-compliant driver in response to detecting thatthe vehicle is driving at least 25 mph over the speed limit, althoughother values are also possible. Speeding non-compliant drivers mayoperate vehicles that drive dangerously fast and are a safety hazard forother road users.

The in-vehicle control computer 150 can further be configured to detectintersection non-compliant drivers. As used herein, an intersectionnon-compliant driver may generally refer to a vehicle that breakstraffic rule(s) in or near an intersection. Intersection non-compliantdrivers are vehicles that break traffic rule(s) at an intersection andin most cases, the in-vehicle control computer 150 can be configured toyield to intersection non-compliant drivers before proceeding throughthe intersection. In some embodiments, the in-vehicle control computer150 can be configured to label a vehicle as an intersectionnon-compliant driver in response to detecting that the vehicle hasbroken one or more of the following rules: proceeding through theintersection without right-of-way, executing an illegal U-turn, lanechanging inside the intersection, and/or turning from Straight Lane.

In response to detecting a non-compliant driver, the in-vehicle controlcomputer 150 can be configured to label, tag, or otherwise remember thenon-compliant driver for a predetermined length of time after thenon-compliant driver has committed a non-compliant maneuver. Thepredetermined length of time may include, for example 10 s, althoughother lengths of time are also possible. Because a non-compliant drivermay be at risk of performing sequential non-compliant maneuvers, bylabeling and remembering the non-compliant driver for the predeterminedlength of time, the in-vehicle control computer 150 can avoid thenon-compliant driver if possible. In some embodiments, the in-vehiclecontrol computer 150 can be configured to retain tags of non-compliantdrivers for the predetermined length of time after the non-compliantmaneuver was performed.

The in-vehicle control computer 150 can be configured to reduce orminimize the amount of time spent driving parallel to a non-compliantdriver. Driving parallel to a non-compliant driver may present a safetyrisk, and thus, the in-vehicle control computer 150 can be configured toavoid driving parallel to non-compliant drivers. The in-vehicle controlcomputer 150 can be configured to break from driving parallel to anon-compliant driver by passing the non-compliant driver or braking.

In one example scenario, the autonomous vehicle 105 can be driving on aroadway and detects a non-compliant vehicle. The in-vehicle controlcomputer 150 can detect the non-compliant vehicle becoming parallel tothe autonomous vehicle 105 and assess options to brake when parallelwith the non-compliant driver. The in-vehicle control computer 150 canthen control the autonomous vehicle 105 to break from driving parallelwith the non-compliant vehicle based on a selected one of the options.FIG. 4H illustrates an example visualization of a vehicle driven by anon-compliant driver 428 (also referred to simply as a non-compliantdriver) parallel to the autonomous vehicle 105 in accordance withaspects of this disclosure.

The in-vehicle control computer 150 can be configured to avoid lanechanging into the lane of a speeding non-compliant driver 428 lane untilafter the non-compliant driver 428 has passed the autonomous vehicle 105in response to detecting the non-compliant driver 428. If the autonomousvehicle 105 were to cut in front of a speeding non-compliant driver, itcould risk a collision and thus, the in-vehicle control computer 150 canbe configured to avoid cutting in front of non-compliant drivers 428,and in particular speeding non-compliant drivers 428, if possible.

In one example scenario, the in-vehicle control computer 150 can detecta speeding non-compliant driver 428 in response to detecting that avehicle is moving at least 25 mph over the speed limit. The in-vehiclecontrol computer 150 can be configured to let the detected speedingnon-compliant driver 428 pass before lane changing. If the in-vehiclecontrol computer 150 intends to make lane change after detecting thespeeding non-compliant deriver 428, the in-vehicle control computer 150can evaluate its target lane with respect to the speeding non-compliantdriver 428. In response to the target lane being the same for theapproaching speeding non-compliant driver 428, the in-vehicle controlcomputer 150 can delay the intended lane change until the speedingnon-compliant driver 428 has passed, unless the lane change is critical.The in-vehicle control computer 150 can then perform the lane changeafter the speeding non-compliant driver 428 has passed the autonomousvehicle 105. FIG. 4I illustrates an example visualization of a speedingnon-compliant vehicle in accordance with aspects of this disclosure.

The in-vehicle control computer 150 can be configured to avoid drivingin a lane adjacent to an oscillating non-compliant driver 428 if aprojected trajectory of the autonomous vehicle 105 will be parallel withthe oscillating non-compliant driver 428. Oscillating non-compliantdrivers 428 may be at risk of entering the autonomous vehicle's 105minimum critical distance. Thus, the autonomous vehicle 105 avoidsdriving in parallel to oscillating non-compliant drivers 428 to maintainthe minimum critical distance.

In one example scenario, the in-vehicle control computer 150 may detectan oscillating non-compliant driver 428 in a vehicle that is swervingbetween both of its lane lines while attempting to keep in the lane. Thein-vehicle control computer 150 can continue on route while avoiding thedetected non-compliant driver 428. The in-vehicle control computer 150can also project the trajectory of the autonomous vehicle 105 andpredict the trajectory of the oscillating non-compliant driver 428. Inresponse to predicting parallel interaction with the oscillatingnon-compliant driver 428, the in-vehicle control computer 150 cancontrol the autonomous vehicle 105 to execute a lane change to avoidbeing parallel with the oscillating non-compliant driver 428. If thein-vehicle control computer 150 predicts no parallel interaction withthe oscillating non-compliant driver 428, the in-vehicle controlcomputer 150 can control the autonomous vehicle 105 to proceed on routewithout instructing any changes to the driving parameters. FIG. 4Jillustrates an example visualization of an oscillating non-compliantvehicle in accordance with aspects of this disclosure.

The in-vehicle control computer 150 can be configured to yieldright-of-way to an intersection non-compliant driver 428. In someembodiments, in response to detecting an intersection non-compliantdriver 428 in or near an intersection that the autonomous vehicle 105plans to proceed through, the in-vehicle control computer 150 can beconfigured to wait to proceed through the intersection until theintersection non-compliant driver 428 has cleared the autonomousvehicle's 105 projected trajectory.

In an example scenario, the in-vehicle control computer 150 can beconfigured to detect an intersection non-compliant driver 428 thatproceeds into or through an intersection when the vehicle does not havethe right-of-way. In response, the in-vehicle control computer 150 canbe configured to yield to the intersection non-compliant driver 428 andthen proceed through intersection after the non-compliant driver 428 hascleared the autonomous vehicle's 105 planned trajectory. If thein-vehicle control computer 150 detects that the intersectionnon-compliant driver 428 comes to stop inside intersection and thein-vehicle control computer 150 confirms that the intersectionnon-compliant driver 428 does not intersect the autonomous vehicle's 105planned trajectory, the in-vehicle control computer 150 can control theautonomous vehicle 105 to proceeds through the intersection withoutwaiting for the non-compliant vehicle to clear the intersection. FIG. 4Killustrates an example visualization of an intersection non-compliantvehicle in accordance with aspects of this disclosure.

The in-vehicle control computer 150 can further be configured to avoidchanging lanes towards any tagged non-compliant drivers 428 unlessrequired to continue on route. The in-vehicle control computer 150 canalso be configured to maintain a recommended lateral distance at alltimes to any non-compliant drivers 428. This is advantageous becausenon-compliant drivers 428 may be unpredictable and a safety risk.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect a static vehicle in the road at a predetermineddistance. The predetermined distance can include, for example, 200 m,222 m, 250 m, 300 m, although other distances are also possible. Thein-vehicle control computer 150 can label any detected static vehicleand inform the oversight system 350 of the detected static vehicleposition in the road.

In response to detecting a static vehicle in front of the autonomousvehicle 105, the in-vehicle control computer 150 can be configured toconsider a critical safety lane change intention and perform a lanechange. If, in response to detecting the static vehicle in front of theautonomous vehicle 105, the in-vehicle control computer 150 determinesthat a lane change is not possible and/or a lane bias level isinsufficient to reach a predetermined lateral gap (e.g., greater than orequal to 1 m, 2 m, 3 m) with the static vehicle, the in-vehicle controlcomputer 150 can be configured to control the autonomous vehicle 105 tostop on a shoulder or emergency lane and/or pull over to the rightmostlane. The in-vehicle control computer 150 can also inform the oversightsystem 350 and activate the hazard lights. The predetermined lateral gapcan include, for example, 1 m, 2 m, 3 m, although other gaps are alsopossible without departing from aspects of this disclosure.

The in-vehicle control computer 150 can also be configured to detect thespeed of vehicles that are located in front, to the right, and to theleft side of the autonomous vehicle 105. In response to the detectedspeed of one of the vehicles being greater than the speed limitindicated by map or traffic signs of the road, the in-vehicle controlcomputer 150 can be configured to classify the vehicle as a dynamicnon-compliant driver 428 and inform the oversight system 350 of theposition of the dynamic non-compliant driver 428. The in-vehicle controlcomputer 150 can also be configured to determine that the speed of thedynamic non-compliant driver 428 in front of the autonomous vehicle 105is less than the speed of the autonomous vehicle 105, and in response,dynamically slow down the speed of the autonomous vehicle 105 tomaintain a higher than normal following distance of a predeterminedamount. The predetermined amount may include, for example, 8%, 10%, 12%higher than normal following distance, although other values are alsopossible.

The in-vehicle control computer 150 can further be configured to detectthe direction of movement of vehicles that are detected in front, to theright, or to the left side of the autonomous vehicle 105. In response tothe detected direction of movement being opposite to the expectedtraffic flow indicated by map or by traffic signs of the road, thein-vehicle control computer 150 can classify the vehicle as a dynamicnon-compliant driver 428 and inform the oversight system 350 of thelocation, direction, and speed of the dynamic non-compliant driver 428.In response to detecting an oncoming dynamic non-compliant driver 428 inthe same lane as the autonomous vehicle 105 and the direction ofmovement being towards the autonomous vehicle 105, the in-vehiclecontrol computer 150 can consider a critical safety lane changeintention and perform a lane change if possible.

Scout Monitoring

Because an autonomous vehicle 105 can function without any operator onboard, the autonomous vehicle 105 may be more vulnerable to possiblehijacking than other vehicles. Thus, the in-vehicle control computer 150can be configured to perform scout monitoring, in which the in-vehiclecontrol computer 150 monitors for entities attempting to determine theautonomous vehicle's 105 load for possible hijacking. As used herein,scouting may generally refer to an unknown entity conducting anassessment to determine the autonomous vehicle's 105 load for possiblehijacking.

In some embodiments, the in-vehicle control computer 150 can beconfigured to perform static scout monitoring including monitoring forother vehicles attempting to determine the autonomous vehicle's 105 loadfor possible hijacking while the autonomous vehicle 105 is parked orstatic. For example, the in-vehicle control computer 150 can detect ascouting event in response to one or more of the following conditionsbeing met: the autonomous vehicle 105 is parked, an unknown vehicle witha person inside or a pedestrian is within a first predetermined distanceof the autonomous vehicle 105, measured from the autonomous vehicle's105 centroid to the unknown vehicle or pedestrian's nearest point, andthe vehicle or pedestrian remains within a second predeterminedthreshold distance of the autonomous vehicle 105 for at least apredetermined length of time consecutively. The first predetermineddistance can include, for example, 8 m, 10 m, 12 m, 15 m, the secondpredetermined threshold distance can include, for example, 8 m, 10 m, 12m, 15 m, and the predetermined length of time can include, for example,10 min, 15 min, 20 min, although other values are also possible.

The in-vehicle control computer 150 can detect a scouting event inresponse to one or more of the following conditions being met: theautonomous vehicle 105 is within the perimeter of a shipping center, anunknown vehicle is within the perimeter of the shipping center that theautonomous vehicle 105 is in, and the unknown vehicle is unauthorizedand unmarked.

The in-vehicle control computer 150 can also detect a scouting event inresponse to one or more of the following conditions being met: theautonomous vehicle 105 is within the perimeter of an autonomous freightnetwork (AFN) terminal, an unknown vehicle is within the perimeter ofthe AFN terminal that the autonomous vehicle 105 is in, and the unknownvehicle is unauthorized and unmarked.

As used herein, dynamic scout monitoring can generally refer to theactivity of monitoring for other vehicles attempting to determine theautonomous vehicle's 105 load for possible hijacking while theautonomous vehicle 105 is moving or dynamic.

In some embodiments, the in-vehicle control computer 150 can detect ascouting event in response to a vehicle being within a predetermineddistance of the autonomous vehicle 105 and the vehicle has a person onits exterior. The predetermined distance can include, for example, 10 m,12 m, 15 m, 17 m, 20 m, although other distances are also possiblewithout departing from aspects of this disclosure.

The in-vehicle control computer 150 can detect a scouting event inresponse to one or more of the following conditions being met: at leasta predetermined number of vehicles (e.g., 3 or more, 4 or more, 5 ormore NPCs) remain parallel, in front of, and/or behind the autonomousvehicle 105 within a predetermined distance (e.g., 15 m, 17 m, 20 m, 22m, 25 m) of the autonomous vehicle's 105 centroid for more than apredetermined length of time (e.g., 4 min, 5 min, 6 min) consecutively,the autonomous vehicle's 105 velocity is greater than a thresholdvelocity (e.g., 25 mph, 30 mph, 35 mph), and all of the vehicles inproximity of the autonomous vehicle 105 maintain a relative velocitywithin a predetermined relative velocity (e.g., 4 mph, 5 mph, 6 mph) ofthe autonomous vehicle 105 for the duration of the period. Thepredetermined number of vehicles can include, for example, 3 or more, 4or more, 5 or more vehicles, the predetermined distance can include, forexample, 15 m, 17 m, 20 m, 22 m, 25 m, the predetermined length of timecan include, for example, 4 min, 5 min, 6 min, the threshold velocitycan include, for example, 25 mph, 30 mph, 35 mph, and the predeterminedrelative velocity can include, for example, 4 mph, 5 mph, 6 mph,although other values are also possible.

The in-vehicle control computer 150 can detect a scouting event inresponse to the autonomous vehicle's 105 connection to the oversightsystem 350 being interrupted or tampered due to an unknown intentionalsource.

In response to detecting any potential scout monitoring activelyoccurring, the in-vehicle control computer 150 can be configured tocontact the oversight system 350 with high priority, at risk alerts.

Oncoming Traffic Detection

Another aspect to the safe navigation of the autonomous vehicle 105includes detecting and responding to oncoming traffic. For example, theautonomous vehicle 105 can be configured to maintain a lateral gap withall oncoming traffic. As used herein, a lateral gap may generally referto a minimum lateral distance between the autonomous vehicle 105 and anyoncoming vehicles 430 or other entities. For example, the minimumlateral distance may be the lateral distance between the closest pointson the autonomous vehicle 105 and the oncoming vehicle 430. In someembodiments, the in-vehicle control computer 150 can be configured toconsider the side mirrors in measuring the lateral gap between theautonomous vehicle 105 and the oncoming vehicle 430. FIG. 4L illustratesan example visualization of a bumper-to-bumper gap and a lateral gap inaccordance with aspects of this disclosure.

The in-vehicle control computer 150 can be configured to measure abumper-to-bumper gap between the front bumper of the autonomous vehicle105 and the front bumper of the oncoming vehicle 430. In someembodiments, the in-vehicle control computer 150 can be configured tocontinuously monitor oncoming vehicles that have a bumper-to-bumperdistance greater than the distance necessary to stop with apredetermined deceleration. The predetermined deceleration may include,for example, −4 m/s2, −5 m/s², −6 m/s2, although other values arepossible. The predetermined deceleration may depend on environmentalfactors such as the road traction.

In one example scenario, the autonomous vehicle 105 and an oncomingvehicle 430 can be travelling on a two-way road at 80 km/h. The timenecessary to stop with a deceleration of 5 m/s² is about 5 s, with adistance to stop around 50 m, and the distance traveled by the oncomingvehicle 430 of around 100 m. In this scenario, the field of view may begreater than about 150 m.

In some embodiments, the in-vehicle control computer 150 can beconfigured to estimate the lateral gap and bumper-to-bumper gap foroncoming vehicles. The in-vehicle control computer 150 can also beconfigured to determine a relative longitudinal speed between theautonomous vehicle 105 and the oncoming vehicle to compute TTC with aderivative of the bumper-to-bumper gap.

The in-vehicle control computer 150 can also be configured to classifyan oncoming vehicle 430 as an oncoming, non-critical safety vehicle inresponse to determining that the predicted lateral gap of the oncomingvehicle 430 at TTC is less than a predetermined upper threshold distanceand greater than a predetermined lower threshold distance. Thein-vehicle control computer 150 can be configured to predict the lateralgap of an oncoming vehicle 430 at the time when bumper to bumper gapequals 0 (e.g., the TTC is equal to 0).

The in-vehicle control computer 150 can also be configured to classifyan oncoming vehicle 430 as an oncoming critical safety vehicle inresponse to predicting the lateral gap of the oncoming vehicle 430 atTTC is less than the predetermined lower threshold distance. In responseto detecting an oncoming critical safety vehicle, the in-vehicle controlcomputer 150 can be configured to consider a critical safety lane changeintention and perform the lane change if possible. In some embodiments,the in-vehicle control computer 150 can be configured to prioritize thefollowing lateral maneuvers to increase or maximize the lateral gap withoncoming vehicle 430 (from highest to lowest) and reduce collisionrisks: lane change and lane bias. The in-vehicle control computer 150can be configured to consider a lane change intention on the oppositeside of oncoming critical safety oncoming vehicle 430 to maximize thelateral gap with the oncoming vehicle 430.

In response to detecting an oncoming critical safety vehicle anddetermining that a lane change is not possible, the in-vehicle controlcomputer 150 can be configured to conduct a critical safety bias awayfrom the oncoming vehicle 430. In response to detecting an oncomingcritical safety vehicle and determining that lane bias level isinsufficient to conduct a critical safety bias away from the oncomingvehicle 430, the in-vehicle control computer 150 can be configured todecelerate with a predetermined longitudinal acceleration and bring theautonomous vehicle 105 to a complete stop. The predeterminedlongitudinal acceleration may include, for example, 4 m/s2, 5 m/s², 6m/s2, although other values are also possible without departing fromthis disclosure.

In response to detecting an oncoming critical safety vehicle, thein-vehicle control computer 150 can be configured to warn the oncomingvehicle 430 with one horn honk and flashing lights until the oncomingvehicle's 430 predicted lateral gap at TTC is greater than apredetermined threshold distance. The predetermined threshold distancecan include, for example, 0.4 m, 0.5 m, 0.6 m, although other values arealso possible. In response to detecting an oncoming critical safetyvehicle and determining that a lane bias level is insufficient to reacha lateral gap greater than a predetermined number of meters, thein-vehicle control computer 150 can be configured to decelerate with apredetermined longitudinal acceleration in order to reduce speed by apredetermined amount. The predetermined number of meters can include,for example, 0.75 m, 1 m, 1.25 m, the predetermined longitudinalacceleration can include, for example, 2 m/s2, 2.5 m/s², 3 m/s2, and thepredetermined amount can include, for example, a reduction by 20%,although other values are also possible.

In response to detecting an oncoming noncritical safety vehicle, thein-vehicle control computer 150 can be configured to consider anon-critical safety lane change intention and perform a lane change ifpossible. In response to detecting an oncoming noncritical safetyvehicle and determining that a lane change is not possible, thein-vehicle control computer 150 can be configured to conduct anon-critical safety bias away from the vehicle.

Example Technique for Modifying Control of an Autonomous Vehicle inResponse to Detecting a Non-Compliant Vehicle

One objective of this disclosure includes controlling an autonomousvehicle 105 in response to the detection of a non-compliant vehicle.FIG. 4M illustrates an example method which can be used to control theautonomous vehicle 105 based on the detection of a non-compliantvehicle. The method 400 may be described herein as being performed byone or more processors, which may include the in-vehicle controlcomputer 150.

The method 400 begins at block 401. At block 402, the in-vehicle controlcomputer 150 is configured to determine that another vehicle isviolating one or more rules of a roadway based on perception datareceived from one or more sensors of an autonomous vehicle. For example,the rule violations that the in-vehicle control computer 150 can beconfigured to detect can include: lane crossing, erratic, speeding,oscillating, intersection, and/or static.

At block 404, the in-vehicle control computer 150 is configured to tagthe other vehicle as a non-compliant driver 428. For example, taggingthe other vehicle may include the in-vehicle control computer 150remembering the non-compliant driver 428 for a predetermined length oftime after the non-compliant driver 428 has committed a non-compliantmaneuver. For example, “remembering” the non-compliant driver 428 mayinvolve the in-vehicle control computer 150 retaining the non-compliantdriver 428 tag for the predetermined length of time. The tag may triggerthe in-vehicle control computer 150 to perform specific planning actionsto avoid the tagged non-compliant driver 428.

At block 406, the in-vehicle control computer 150 is configured tomodify control of the autonomous vehicle in response to tagging theother vehicle as a non-compliant driver 428. For example, the in-vehiclecontrol computer 150 can be configured to avoid driving parallel to thetagged vehicle for the predetermined length of time. The method ends atblock 408.

Operational Zones

In some embodiments, the digital map with high precision positioningdata and information for roadways and surroundings is pre-developed andstored in the memory 175 of the in-vehicle control computer or vehiclecomputer unit (VCU) 150 of the autonomous truck or autonomous vehicle(AV) 105 shown in FIG. 1 . For the context of this disclosure, thedigital map is also referred to as the map. When the autonomous vehicle105 traverses on roadways on a mission, the in-vehicle control computer150 may identify different types of operational zones based on mappeddata or the real time data detected from the vehicle sensor subsystems144, including one or more camera, the radar and the LIDAR devicesinstalled on the autonomous vehicle 105.

Location-Driven Traffic Law Library

For driving in different jurisdictions or regions (e.g., differentstates, different provinces, or different countries), each region mayhave its own rules of the road that may include different traffic lawsand informal rules. Although some of the rules of the road may besimilar in the regions, there exist differences. For the autonomousvehicle 105 to cross from one region into another, it has to master therules of the road in each region that have been developed over time, sothat the vehicle can facilitate the orderly and timely flow of trafficin local areas. This is done by developing a digital map that includesmetadata of local rules of the road of each region, and store the datain a location-driven traffic law library. As such, when entering aregion, the autonomous vehicle may retrieve the local traffic law andcustomary driving data to effectively localize itself. For each region,the autonomous vehicle 105 may demonstrate the same localizationcapability by storing the metadata including the rules of the road ofdifferent regions in the digital map system that may be stored on thein-vehicle control computer 150 and/or other components of theautonomous vehicle 105.

While the autonomous vehicle 105 may follow the local traffic laws inall aspects, a few aspects may be uniformly shared by the regions. Forexample, the autonomous vehicle 105 may follow the right-of-wayprinciple for any vehicle coming on a road, a lane or an intersection.The right-of-way principle defines the legal right of one vehicle'spassage over the shared roadway with another vehicle. The autonomousvehicle 105 may use turn signals to indicate that it intends to turn apredetermined length of time before it makes the turn, e.g., at least 8seconds, 10 seconds, or 12 seconds before turning. And the autonomousvehicle 105 may give priority to a level crossing (railway crossing).There are level crossings without a level crossing barrier or signal,e.g., as shown in the photo in FIG. 5A as an example. The autonomousvehicle 105 can be configured to give priority to any transportationsystem in the presence of a level crossing. Further, the autonomousvehicle 105 may follow speed limit traffic signs and drive within thespeed limit. FIG. 5B shows an example speed limit traffic sign for alocal roadway. When a do-not-pass traffic sign is detected by thevehicle sensor subsystems 144, the in-vehicle control computer 150 onthe autonomous vehicle 105 may decide to avoid overtaking any vehicles.A do-not-pass sign is shown in FIG. 5C as an example.

In summary, the oversight system 350 may maintain a product requirementdocument (PRD) that contains the traffic rules for each region, andapply modifications to the PRD traffic law library based on eachregion's updated laws. As such the oversight system 350 may establish aset of traffic rules exceptions that apply to each region, anddistribute the region traffic rule expectations to each autonomousvehicle 105 on the road.

Autonomous Vehicle Localization

Since the autonomous vehicle 105 is self-driving and may be unmanned,the in-vehicle control computer 150 may track its location andmovements. This is usually fulfilled in a few different ways, includingdetection of lane markings or stationary and moving objects using thesensors on the autonomous vehicle 105, receiving global positioningsystem (GPS) signals, deriving location from the data generated fromvisual odometry (VO) or inertial measurement unit (IMU) sensors on theautonomous vehicle 105, applying a dead-reckoning algorithm, retrievingfrom the digital map stored on the in-vehicle control computer 150, andetc. The in-vehicle control computer 150 of the autonomous vehicle 105may leverage and fuse some or all available data in order to accuratelydetermine the autonomous vehicle's location. In addition, the autonomousvehicle 105 may monitor the error of each data source for localizationand prioritize the data sources for data fusion.

The autonomous vehicle 105 may detect lane markings for the lane itdrives in to decide its location. The autonomous vehicle 105 may detectall lanes of the roadway and use the lane markers and map information todetermine the lateral distance between the autonomous vehicle 105 andthe road borders, and as such decide its lateral location on theroadway.

In some embodiments, the autonomous vehicle 105 may detect stationaryand moving objects to determine its location. This may involve using avisual inertial odometry (VIO) 3D vision as a source to determine theautonomous vehicle's location. The VIO method becomes important when GPSsignal is weak or unavailable, for example, inside a tunnel or anunderground parking garage.

In some embodiments, GPS signal can be used to accurately locate theautonomous vehicle 105, which is equipped with GPS receiving antennas toreceive GPS signals. The GPS signal can be used for global longitudinalpositioning, e.g., in the length direction, on the roadway. However, theGPS signal data also contain position accuracy parameters, such ashorizontal dilution of precision (HDOP), position dilution of precision(PDOP), and geometric dilution of precision (GDOP). In some embodiments,the autonomous vehicle 105 may use the HDOP value of the GPS signalparameters to monitor the localization accuracy of the GPS signal. Toimprove the performance of GPS positioning, including signal strengthand positioning accuracy, the autonomous vehicle 105 may have aplurality of GPS modules and/or antennae located on different parts ofthe autonomous vehicle 105.

Another method of location determining is IMU localization. Theautonomous vehicle 105 may use the angle and acceleration data generatedfrom IMU sensors and a dead-reckoning algorithm to update its location.In some embodiments, the autonomous vehicle 105 may retrieve the mappeddata, e.g., road width, lane boundaries, lane widths, number of lanes,etc., to improve confidence and accuracy of localization.

In some embodiments, the in-vehicle control computer 150 continuouslymonitors localization error in order to obtain a high localizationconfidence and accuracy. For example, the in-vehicle control computer150 may define a predetermined minimum required localization accuracy,in both lateral and longitudinal directions on the roadway, to completedifferent tasks.

As an example, if localization accuracy of the received GPS signaldegrades on a highway, the autonomous vehicle 105 may plan to drivestraight. In other words, the autonomous vehicle 105 may continuedriving in the current lane using lane markers detected by the equippedsensors until it regains localization of the GPS signal. In someembodiments, the autonomous vehicle 105 may perform a minimal riskcondition (MRC) maneuver to pull over to a shoulder and call for backupfrom the oversight system 350 if higher accuracy in localization isrequired to navigate through the path, e.g., if the autonomous vehicle105 needs to take an oncoming exit, or to operate in local roads, andetc.

In some embodiments, if there is a loss of GPS signal, e.g., whendriving in a tunnel, the autonomous vehicle 105 may obtain localizationdata from other means and continue its path without having a GPS signal,if the in-vehicle control computer 150 determines it is safe to do so.For example, the autonomous vehicle 105 may use location data generatedby VIO, which is a fusion of visual odometry (VO) and inertiameasurement unit (IMU), a dead-reckoning algorithm, and map data, toupdate the autonomous vehicle's 105 local and global positions. In someembodiments, the autonomous vehicle 105 may continue on its path withoutGPS signal if the autonomous vehicle 105 has sufficient localizationaccuracy for its upcoming driving tasks from other localization sourcessuch as VIO. For example, the autonomous vehicle 105 may have asufficient localization accuracy when the localization accuracy isgreater than a predetermined minimum required accuracy.

In some embodiments, the autonomous vehicle 105 may pull over safely andcall for backup from the oversight system 350 if the autonomous vehicle105 doesn't have the required localization accuracy to complete itsdriving tasks or is unable to recover its localization. In someembodiments, the autonomous vehicle 105 may pull over safely and callfor backup from the oversight system 350 if the autonomous vehicle 105loses lateral localization. As used herein, lateral localization maygenerally refer to the ability of the autonomous vehicle 105 to staywithin lane boundaries. Hardware failures can cause non-recoverable lossof localization, in which none of the localization sources can providesufficient localization accuracy to complete the driving tasks.

Localization Constraints

In some embodiments, when GPS signal becomes weak and the in-vehiclecontrol computer 150 is able to guide through the weak signal area, theautonomous vehicle 105 may keep driving in its current lane, but may notcross into adjacent lanes occupied by other vehicles.

In some embodiments, if the autonomous vehicle 105 has lateraloscillation that exceeds the lane dividing lines which are used toseparate traffic, the in-vehicle control computer 150 may considerperforming an MRC maneuver, i.e., to pull over to the shoulder. In someembodiments, if the autonomous vehicle 105 is unable to recognizelocation of the shoulder, the in-vehicle control computer 150 may decideto stop in the current lane without crossing into an adjacent lane. Forexample, the in-vehicle control computer 150 can be configured tocontrol the autonomous vehicle 105 to stop with a deceleration that isless than a threshold deceleration to avoid potential damage.

In some embodiments, the in-vehicle control computer 150 may decide notto continue moving the autonomous vehicle 105 on the roadway if the GPSsignal is less than a threshold strength for longer than a predeterminedlength of time in order to reduce the chance of causing property damageor being involved in a crash. The in-vehicle control computer 150 mayconsider entering the first MRC maneuver, e.g., to pull over to theshoulder, if the GPS signal is less that the threshold signal strengthfor longer than a predetermined length of time, e.g., 2 minutes, 3minutes, or 5 minutes, although other values are also possible.

Road Grade Tracking

Most of the roadways are not perfectly horizontal. For example, roadwaysmay have a grade in the longitudinal direction of the road. This gradecan also be called slope or gradient. The grade of a road can bedetermined by the tangent of the angle formed by the road surface(assuming the road surface is laterally even) to an imaginary horizontalplane. In FIG. 5D, when a vehicle climbs the graded road segment frompoint A to point B for a road distance I, this results in a verticalrise Ah and a horizontal movement d. An angle formed between roadsurface and the horizontal line is a, as shown in FIG. 5D. The grade ofthe road is calculated as the ratio of the vertical rise Ah to thehorizontal distance d, as expressed by the following equation:

$\sigma = {100 \times \frac{\Delta h}{d}\%}$

For example, if the autonomous vehicle 105 drives on a segment of a roadrising 15 meters per 100 meters of a horizontal movement, thecorresponding grade of the sloped road segment is 15/100=15%.

By definition, driving an uphill road means climbing a positive grade,and driving downhill means descending along a negative grade. The highprecision map stored in the memory 175 of the in-vehicle computer 150can be configured to include the grade information, including theabsolute grade value, the grade sign in positive (uphill) or negative(downhill) and the grade length (how many miles it occurs), for eachsegment of the roadways.

When traversing on roadway, the autonomous vehicle 105 may detect gradetraffic signs, such as the signs shown in FIGS. 5E-2L as examples. Assuch, the in-vehicle control computer 150 may obtain gradecharacteristics, including grade value, grade length and grade sign,from the grade traffic signs or the mapped data.

The autonomous vehicle 105 may also detect grade traffic signs withcomplementary information related to driving advice. For example, thetraffic signs shown in FIGS. 5M-2Q give complementary driving advice oradvisories, e.g., “slow” to indicate slow down, “use low gear” tosuggest activating the lowest or a lower gear, “check brakes” to ensurethat brakes are efficient, and truck speed limits in case the autonomousvehicle 105 is a truck. Some grade traffic signs may have information toinform the presence of a specific turnout for trucks or slow vehicles,so that the vehicles may enter a turnout area to let traffic behind topass safely.

The autonomous vehicle 105 may trigger pre-developed plans stored on thememory 175 when driving graded roadways. For example, the autonomousvehicle 105 may be configured to operate on roadways with a maximum roadgrade up to a predetermined grade limit, e.g., 7%, 8%, or 9%. If aroadway having a road grade greater than or equal to the predeterminedgrade limit, the autonomous vehicle 105 may trigger a first MRC maneuverand stop at a shoulder. When a high grade value traffic sign of apredetermined high grade value, e.g., 5%, 6%, 7%, or more is detected,but less than the predetermined grade limit, the autonomous vehicle 105may plan to make lane changes so as to position the autonomous vehicle105 in the right-most lane. When the detected road grade is less thanthe predetermined high grade value, the autonomous vehicle 105 mayfollow the speed limit indicated by road grade traffic signs orretrieved from the map. In embodiments where the autonomous 105 vehicleis a truck, the autonomous vehicle 105 may use a truck turnout lane inresponse to detecting critical situations, such as taking a turn on aroad with an acute curve and grade.

When approaching a negative grade, the autonomous vehicle 105 may slowdown, e.g., by using a lower or the lowest gear to slow down the vehicleby a predetermined number of meters per square second, e.g., 0.5 m/s², 1m/s², or 1.5 m/s², until slowing down to within the speed limit. A lowergear enables higher engine speed that results in a higher engine brakingtorque, and thus engine braking. Slowing down to a lower speed reducesthe braking distance for a safe driving. Therefore, on a road with anegative grade, the autonomous vehicle 105, particularly when theautonomous vehicle 105 is a truck, may first use engine braking andemploy slip control strategies to manage speed control. In theembodiments that the autonomous vehicle 105 is a truck or large vehicle,emergency braking, e.g., foundation braking plus engine braking, may beapplied when the autonomous vehicle 105 is on a negative grade road andan obstacle is detected at a distance less than a predetermineddistance.

In some embodiments, when approaching a positive grade road, theautonomous vehicle 105 may plan to drive at a desired speed byincreasing throttle or acceleration to compensate for the positive gradeeffect.

Road Superelevation Tracking

FIG. 5R is a schematic illustrating an example superelevation roadwaywhere the roadway makes a turn. Depending on the curvature of the turnand the speed of a driving vehicle, a large centrifugal force maydevelop, and this centrifugal force may cause the vehicle to skid oreven overturn. As illustrated, superelevation in a road is a transverseinclination provided to the curve portion of a roadway in which theoutside edge of the road or pavement is raised with respect to thecenter line and the inside edge so as to allow fast-moving vehicles tosafely pass without overturning and skidding.

As shown in FIG. 5R, before entering a superelevation roadway segment, acenter line 502 that is in the middle of the roadway is typically higherthan an inside edge 503 and an outside edge 504, as illustrated at startpoint 506. As such, the roadway forms an amber laterally, allowingrainwater to drain off the road. The start-point 506 indicates where acrown road section 512 begins. From this point on, the banking of theroadway starts to tilt laterally, and the height of the outside edge orpavement 504 starts to rise relative to the center line 502, but remainslower than the center line 502. This means that although the road camberrate changes, the camber still exists. Then, it comes to a firsttransition point 508 where a level road section 514 starts. At thistransition point 508 the outside edge 504 is level with the center line502. From the first transition point 508 on, the banking of the roadwayis inclined laterally from the outside edge 508 to the center line 502and to the inside edge 503. The inclination rate continues to increaseuntil the road comes to a curved section which is a full superelevationroadway section 515. The full superelevation roadway section 515 startsat a first full superelevation point 510 and ends at a second fullsuperelevation point 511. Within the full superelevation roadway section515, the outside edge 504 is at a maximum height relative to the centerline 502 and the inside edge 503. This higher inclination rate definesthe superelevation rate of the superelevation roadway section 515. Thenas the roadway straightens, it comes to a second level road sectionwhich ends at a second transition point 518, which is equivalent to thefirst transition point 508. The roadway continues at a second crownroadway section, which ends at an end point 516, which is equivalent tothe start point 506. Therefore, when a vehicle, e.g., the autonomousvehicle 105, drives through the roadway sections in FIG. 5R, it followsthe direction illustrated by the block diagram shown in FIG. 5S, andgoes from the crown section (in) 512, and the level section (in) 514 tothe superelevation section 515, and then the level section (out) andfinally to the crown section (out).

The crown sections and the level sections, as shown in FIG. 5R, are alsocalled the runout sections and the runoff sections. Therefore, FIG. 5Scan also be described as follows: the vehicle follows the direction andgoes from the runout section (in) 512, and the runoff section in 514 tothe superelevation section 515, and then the runoff section (out) andfinally to the runout section (out).

The digital map stored on the in-vehicle control computer 150 maycontain superelevation roadway section information, including type,e.g., crown section, level section, full superelevation section, sectionlength and rate, e.g., camber rate, inclination rate, and superelevationrate.

The autonomous vehicle 105 may detect the superelevation roadwaysections, e.g., crown section, level section, full superelevationsection, using vertical signs, mapped information, or using the inertialmeasurement unit (IMU) equipped on the vehicle.

According to mapped data and detected roadway data, the in-vehiclecontrol computer 150 may develop plans when approaching a superelevationroadway. For example, when the superelevation rate of the fullsuperelevation section is less than a predetermined threshold value,e.g., 3%, 4%, or 5%, the autonomous vehicle 105 may bypasssuperelevation road detection and proceed on its path. In someembodiments, when the superelevation rate is within a predeterminedrange, e.g., greater than 4% and lower than 9.5%, greater than 3% andlower than 10%, or greater than 2.5% and lower than 10.5%, theautonomous vehicle 105 may identify the road as a superelevation road.

When the autonomous vehicle 105 detects a crown section before asuperelevation section, e.g., approaching a superelevation roadway,within a predetermined distance, e.g., in the next 400 meters, 500meters, or 600 meters, it may dynamically adjust its speed to maintain aminimum lateral skidding. When the autonomous vehicle 105 detects alevel section ahead of a superelevation section, e.g., when just enteredthe leading crown section, within a predetermined distance, e.g., in thenext 75 meters, 100 meters, or 125 meters, it may maintain its speedaccording to a predetermined speed formula, which may depend on theroadway superelevation rate. It is possible that a single maximumsuperelevation rate may not be universally applicable.

When the autonomous vehicle 105 detects a superelevation section beforea crown section, e.g., in the direction leaving the superelevation roadsection, within a predetermined distance, e.g., in the next 400 meters,500 meters, or 600 meters, it may increase its speed according to apredetermined formula.

Accident Area

The in-vehicle control computer 150 defines an accident area as an areaof a roadway which is blocked by stationary vehicles, authorities, orunknown objects. The accident area may include all lanes of the roadwaythat are partially or entirely blocked, and may include blocked shoulderor edge of the roadway.

Before approaching an accident area, the in-vehicle control computer 150on the autonomous vehicle 105 may actively detect a potential accidentarea from a predetermined minimum distance, e.g., at least 400 meters,450 meters, 500 meters, 550 meters, or 600 meters away. The in-vehiclecontrol computer 150 may detect and follow the guiding signs at the siteto safely navigate through the accident area.

In addition to real time detection using the vehicle sensor subsystems144 on the autonomous vehicle 105, the in-vehicle control computer 150may receive updated traffic conditions from the oversight system 350 viathe wireless network 370 as incident information becomes available fromlocal traffic authorities in real-time. Incidents are reported byincident command system (ICS), national incident management system(NIMS), and other authorities. The oversight system 350 may get thisinformation as soon as it becomes available, and passes it to theautonomous vehicles 105 in the incident area.

The in-vehicle control computer 150 of the autonomous vehicle 105 may beable to detect an accident area by different means. First, thein-vehicle control computer 150 may perceive stationary vehicles and theassociated lane(s), shoulder, or edge of the road occupied by thestationary vehicles, from a predetermined minimum distance, e.g., atleast 400 meters, 450 meters, 500 meters, 550 meters, or 600 meters awayfrom the site. The stationary vehicles may include other vehiclesstopped as first responders and not involved in the actual accident.This programmed proactive approach allows the in-vehicle controlcomputer 150 to get firsthand real time information early andindependently, and react accordingly when arriving at the accident area.Secondly, the in-vehicle control computer 150 may detect unknownstationary objects and the associated lane(s), shoulder, or edge of theroad that are blocked by the objects from a predetermined minimumdistance, e.g., at least 400 meters, 450 meters, 500 meters, 550 metersor 600 meters, away from the site. Unknown objects are defined asobjects that the in-vehicle control computer 150 is not able toclassify. Examples of unknown objects include vehicles on their sides,vehicles upside down, trailers on the ground, cargo on the ground, etc.

Another detection is for emergency vehicles. In some embodiments, thein-vehicle control computer 150 may proactively detect the stationaryemergency vehicles from a predetermined minimum distance, e.g., at least400 meters, 450 meters, 500 meters, 550 meters, or 600 meters away. Thein-vehicle control computer 150 may also detect amber strobes orflashing lights affixed to the emergency vehicles from a predeterminedminimum distance, e.g., at least 400 meters, 450 meters, 500 meters, 550meters, or 600 meters away.

In some embodiments, the in-vehicle control computer 150 may detectcones and the associated lane the cones are placed for from apredetermined minimum distance, e.g., at least 100 meters, 125 meters,150 meters, or 175 meters away. In some embodiments, the in-vehiclecontrol computer 150 can detect the positions of the cones inside a lanewith high accuracy, e.g., within 0.3 meter, 0.4 meter, 0.5 meter, or 0.6m, when the autonomous vehicle 105 is located within a predetermineddistance to the cones, e.g., within 40 meters, 50 meters, or 60 meters.The in-vehicle control computer 150 may use the positions of the conesfor navigating through the accident area.

In some embodiments, the in-vehicle control computer 150 can detectpeople in lanes, shoulder, and on edge of the road from a predeterminedminimum distance, e.g., at least 200 meters, 225 meters, 250 meters, or275 meters away.

In some embodiments, the in-vehicle control computer 150 can detect thehand gestures (e.g., to guide traffic) of law enforcement officers froma predetermined minimum distance, e.g., at least 25 meters, 40 meters,50 meters, 60 meters, or 75 meters away. Officers may use universal handsignals and gestures for manual traffic direction and control. Two basichand signals are commonly used. Officers may use an open-hand, palm-outsign to indicate stop. To allow the stopped traffic to proceed, officersmay point towards the first stopped vehicle, and then use the other handto motion the driver to proceed. The autonomous vehicle 105 may followthe guidance from law enforcement authorities to stop or navigatethrough accident areas.

The in-vehicle control computer 150 may detect mobile traffic signs andvariable message signs (VMS) from a predetermined minimum distance,e.g., at least 75 meters, 100 meters, or 125 meters away. Differentkinds of mobile traffic signs and variable message electronic signs arecommonly used in accident areas to alert oncoming vehicles, and to guidethe vehicles through the accident area.

Furthermore, the in-vehicle control computer 150 may detect road flaresand their associated lane(s) at nighttime from a predetermined minimumdistance, e.g., at least 175 meters, 200 meters, 250 meters, or 275meters away. Road flares are commonly used by drivers in accident areasto alert oncoming vehicles. It is recommended that road flares areplaced 100-150 meters away from the accident area towards the oncomingtraffic.

After detection of an accident area, the in-vehicle control computer 150may decide to navigate the autonomous vehicle 105 through the accidentarea autonomously when it is safe to do so. Or the autonomous vehicle105 may stop in a safe area when it is not safe to pass the accidentarea.

In some embodiments, when approaching an accident area, the autonomousvehicle 105 may proceed in such a way to leave at least one full vacantlane distance from its vehicle body to the accident area if it is safeto do so. The autonomous vehicle 105 may slow down to a predeterminedsafe speed and maintain the speed when it approaches the accident area.Further, the autonomous vehicle 105 may merge into the farthest lanefrom the accident area. In some embodiments, the autonomous vehicle 105may safely stop when it is not possible, or unsafe, to pass the accidentarea. If driving in a two-lane road in which a lane is blocked by anaccident, or driving in a single-lane road in which the shoulder isblocked by an accident, the autonomous vehicle 105 may slow down to apredetermined speed, e.g., 15 mph, 20 mph, or 25 mph, and pass theaccident area. The move over laws may be different by region. To obey amove-over law, the autonomous vehicle 105 may vacate the lane closest tothe accident area if it is safe to do so. If the road has only two lanes(or one lane with accident area in the shoulder), the autonomous vehicle105 may slow the speed to 20-30 mph.

The autonomous vehicle 105 may follow the traffic flow as they navigatethrough and pass the accident area. The autonomous vehicle 105 maychange lanes if required when following the traffic flow.

In some embodiments, when all lanes are blocked the autonomous vehicle105 may stop on the shoulder of the road on the side that is closer toits current lane if it is safe to do so. In some embodiments, thevehicle may stop safely in its current lane when it is not safe tochange lanes or to move over to the shoulder or edge of the road. Insome embodiments, the vehicle may stop at least 50 meters before or awayfrom the accident area if it is safe to do so. The in-vehicle controlcomputer 150 on the autonomous vehicle 105 may communicate with theoversight system 350 after full stop to get guidance.

In some complex situations, the autonomous vehicle 105 may stop on theshoulder of the road on the side that is closer to its current lane ifit is safe to do so. In some embodiments, the in-vehicle controlcomputer 150 on the autonomous vehicle 105 may communicate with theoversight system 350 via the wireless network 370 and request forbackup. In some complex situations, action may be either unclear orhighly risky for the autonomous vehicle 105. Examples of the complexsituations may include blocked road because of mega accidents orinfrastructure damage, e.g., falling off a bridge, low road visibilitydue to smoke, and etc.

If the autonomous vehicle 105 has completely stopped and waited due to acomplex situation, the autonomous vehicle 105 may communicate with theoversight system 350 and seek guidance before moving again.

Road Blockage Classification and Handling

In some embodiments, in the digital map stored in the memory 175 of thein-vehicle control computer 150, a road blockage is defined as atemporary closure of a road where the transit of vehicles is notpermitted. A full road closure is designed to eliminate the exposure ofvehicles to work zones and workers by temporarily closing a facility forrehabilitation, maintenance, or emergency tasks.

The in-vehicle control computer 150 of the autonomous vehicle 105 maydetect traffic signs that indicate the presence of a road blockage.Examples of road blockage signs are shown in FIGS. 5T-2V. In someembodiments, once road blockage signs are detected, the in-vehiclecontrol computer 150 may inform the oversight system 350. The in-vehiclecontrol computer 150 may detect any real time differences between whatis perceived by the vehicle sensor subsystems 144 and what is mapped.When a conflict is detected between the mapped data and perceived datafor a road blockage, the in-vehicle control computer 150 may inform theoversight system 350. In some embodiments, the in-vehicle controlcomputer 150 may continuously update the map stored in the memory 175with road blockage information, and the location of the known roadblockage.

Traffic control devices (or road blockage devices) are defined in themap as road barriers, circular flashing amber lights, cones, barricades,etc., as shown in FIGS. 5W-2Y as examples. The in-vehicle controlcomputer 150 may detect traffic control devices for road blockage at apredetermined distance from the devices, e.g., at 200 meters, 222meters, or 250 meters.

When a road blockage is detected and no alternative route is available,the autonomous vehicle 105 may stop on a shoulder or the emergency lane,or pull over to the rightmost lane, and inform the oversight system 350.The autonomous vehicle 105 may activate its hazard lights. However, whena detour is indicated by traffic signs, the autonomous vehicle 105 mayfollow the direction indicated by the signs.

Parking Lot

The in-vehicle control computer 150 may operate the autonomous vehicle105 in outdoor parking lots. Also, the in-vehicle control computer 150may be able to park the autonomous vehicle 105 in a designated parkingspots on the pre-planned route as determined by a base station, or bytemporary and dynamic navigation planning.

After parking, the in-vehicle control computer 150 may notify the basestation with the vehicle's location and health report. If the vehicle istemporarily parked and the trip is not over, the base station may beable to remotely resume the trip over the wireless network 370.Otherwise, if the trip is over, the in-vehicle control computer 150 mayautomatically turn off the system and engine.

Double triggers are required to start an autonomous trip for the system.

Unmapped Construction Zone

The digital map stored in the memory 175 of the in-vehicle controlcomputer 150 on the autonomous vehicle 105 may be updated continuouslyor periodically to include the newest developments on roadways. However,new construction zones or those construction zones in remote areas maynot be in the digital map. An unmapped construction zone is defined as asection of the roadway that is not indicated in the map as aconstruction zone, but with active roadwork going on that may involve alane closure, detour, or having moving equipment. Upon detection of anunmapped construction zone during a mission, the in-vehicle computer 150of the autonomous vehicle 105 may report the unmapped construction zoneto the oversight system 350.

In some embodiments, the autonomous vehicle 105 may use an alternateroute if available and avoid entering into the construction zone asindicated by the traffic signs while approaching an unmappedconstruction zone. FIG. 5Z and FIG. 5AA show examples ofconstruction-zone road signs. The in-vehicle control computer 150 maynotify the oversight system 350 when a detour is required because of anunmapped construction zone.

The in-vehicle control computer 150 may detect traffic signs at theconstruction zone and navigate through the zone by prioritizing the useof the virtual walls over the lane lines. FIG. 5AB and FIG. 5AC showexamples of traffic signs and traffic control or channeling devices.When these traffic signs and the traffic control devices are detected,the in-vehicle control computer 150 models a virtual wall forconstruction zone that virtually separate the construction zone from thedrivable roadways. Traffic signs and channelizing devices may includecones, barrels, signs, large vehicles, or construction zone flaggers orworkers in bright colored vests, as shown in FIGS. 5AD-2AJ as examples.The autonomous vehicle 105 may follow the corresponding directionsindicated by the signs, the devices, and signals from the zone flaggers.

Approaching a construction zone, the in-vehicle control computer 150 maydetect the start of a construction zone by detecting the relevantconstruction zone traffic signs. For example, when a road-work-aheadtraffic sign shown in FIG. 5AK is detected, it means that the autonomousvehicle 105 is approaching a construction zone. When a road sign withreduced speed limit is detected by the in-vehicle control computer 150in a work zone, the autonomous vehicle 105 may dynamically adjust thespeed according to the traffic signs. For example, the road signs shownin FIG. 5AL indicate that the speed in the work zone is reduced from 70kmph to 50 kmph. An end of the construction zone may be an area thatmeets conditions predefined in the in-vehicle control computer 150 ofthe autonomous vehicle 105.

As shown in FIGS. 5AM-2AN, photos of example human controllers orflaggers, and FIGS. 5AO-2AT, schematics of human controllers orflaggers, traffic control signs and/or devices may be held by humancontrollers or construction zone flaggers to control traffic flow. Humancontrollers may use hand signals to stop traffic, slow down traffic, orallow traffic to proceed.

After entering a construction zone, the autonomous vehicle 105 may drivein the right lane when available. As shown in FIG. 5AU, the in-vehiclecontrol computer 150 may detect the white solid stop lines whilenavigating a construction zone. And there may be traffic signs, like thebe-prepared-to-stop sign shown in FIG. 5AV, to caution the autonomousvehicle 105 to prepare to stop. Further, the autonomous vehicle 105 mayplan a lane change into a lane that is not behind marker cones when thecones are to indicate lane merging within the construction zone. Theautonomous vehicle 105 may plan a lane shift as required within theconstruction zone by following the traffic signs and virtual walls. Laneshifting is indicated in FIG. 5AW by solid white lines, and in FIGS.5AX-2BA by on road and traffic signs.

The in-vehicle control computer 150 may detect motorized traffic in aconstruction zone that moves from an area behind barricades or trafficcones and coming in the trajectory of the autonomous vehicle 105 throughthe opening in those barriers. FIG. 5BB shows an example traffic signindicating that there may be truck movement in the work zone. Theautonomous vehicle 105 may prepare for a full stop or slow down to avoida collision.

Interchange Navigation

When developed, the map stored in the memory 175 of the in-vehiclecontrol computer 150 intends to include the locations of all theinterchanges and their types. The types of interchanges may include adiamond interchange, a full cloverleaf interchange, a partial cloverleafinterchange, a trumpet interchange, a three-leg directional interchange,a four-leg all-directional interchange, a semi-directional interchange,a single entrance and/or exit interchange (partial interchange), and asingle point interchange (SPI). There are more interchange types thatare not included in the above list, e.g., a double crossover diamondinterchange, a displaced left turn interchange, a diverging diamondinterchange, and etc.

The in-vehicle control computer 150 may perceive an interchange withdifferent attributes, including on ramp/off ramp, ramp curvature, mergein/merge out lanes, dedicated lane for divergence, presence of trafficsignals, and etc. In some embodiments, the in-vehicle control computer150 may detect ramp properties such as straight, curved, angle ofcurvature, at least from a predetermined distance away, e.g., at least200 meters, 220 meters, 250 meters, 270 meters, or 300 meters away.

The in-vehicle control computer 150 on the autonomous vehicle 105 maydetect the type of the interchange it approaches. For example, thein-vehicle control computer 150 may determine that the interchange is adiverging diamond interchange when the in-vehicle control computer 150detects an off ramp first and there are traffic control devices such aslights, pavement markings, or physical barriers, at the interchange.FIG. 5BC schematically illustrates a diverging diamond interchange andtraffic flow directions.

At a diverging diamond interchange, the autonomous vehicle 105 mayfollow the traffic signal and change lanes to the left side of the roadin the interchange. If the autonomous vehicle 105 wants to continue onthe same road, it may continue on the road. In case the autonomousvehicle 105 wants to exit and get on to the other freeway, it may takethe left lane and exit the road to merge into the other road.

An example full cloverleaf interchange is shown FIG. 5BD. The in-vehiclecontrol computer 150 may determine that the interchange is a fullcloverleaf intersection when the following conditions are met: the offramp merge is encountered first; the on-ramp lane merges into thefreeway next; each freeway is connected with two circular ramps with270° turns exist next to the interchange with each of them going in anopposite direction.

At a cloverleaf interchange, the autonomous vehicle 105 may approach andtake the first off ramp to interchange onto the other freeway. Theautonomous vehicle 105 may plan to enter the ramp at the speed limitindicated by the mapped data or detected from a real time traffic sign.When the autonomous vehicle 105 crosses the first off ramp and intendsto take the next 270° circular off ramp, it may plan to enter the rampat a speed that is a predetermined percentage, e.g., 10%, 12%, 15%, 18%,lower than the speed limit and dynamically adjust the speed tocompensate for the gradient and maintain a predetermined lateraldeceleration, e.g., no more than 1 m/s², 2 m/s², or 3 m/s².

When merging from a ramp onto a highway, the autonomous vehicle 105 maymerge with a non-critical safety lane change intention. The vehicle mayuse appropriate light indication to let oncoming or following vehiclesbe aware of its intention to merge and to change lane. When merging froma circular ramp to a highway, the autonomous vehicle 105 may perform azipper merge. An example zipper merge is shown in FIG. 5BE.

Intersection Roundabout

In some embodiments, a roundabout is a circular intersection wheretraffic travels around a central island in a counter-clockwise (orclockwise depending on the laws of the region) direction that is definedand saved in the digital map on the in-vehicle control computer 150.Roundabouts are categorized based on the number of lanes and inscribedcircle diameter, as shown in the table in FIG. 5BF.

In some embodiments, the digital map stored on the in-vehicle controlcomputer 150 may contain data of certain roundabouts but not allroundabouts. For example, a roundabout may be mapped if the inscribedcircle diameter of the roundabout is wider than a predetermined numberof feet, e.g., 75 ft, 100 ft, or 125 ft. And the map may contain datafor all multi-lane roundabouts.

In some embodiments, the autonomous vehicle 105 may map urbansingle-lane roundabouts with truck apron, urban double-lane roundabouts,rural single-lane roundabouts with truck apron, rural double-laneroundabouts. Truck apron which effectively makes the roundaboutmulti-lane is the area near the center and around the island of theroundabout that gives large vehicles extra space to accommodate thevehicle turning path as the vehicle makes its way through theroundabout. The apron is slightly elevated and visually different fromthe circulating roadway.

Mini roundabouts, i.e., smaller roundabouts that is not mapped by thedigital map for the autonomous vehicle 105, may have a sign with a bluecircle and white arrows circling clockwise, as shown in FIG. 5BG.

The in-vehicle control computer 150 of the autonomous vehicle 105 maydetect an oncoming roundabout sign from at least 150 meters away. Thein-vehicle control computer 150 may detect the type and number of lanesof an oncoming roundabout from at least a predetermined distance, e.g.,75 ft, 100 ft, or 125 ft.

In some embodiments, the in-vehicle control computer 150 may detecttraffic signs and/or pavement markings that guide or prohibit certainmovements. A yield ahead sign may be used on all approaches to aroundabout in advance of the yield sign. A circular intersection sign ora roundabout ahead sign may be installed on each approach in advance ofthe roundabout. Different roundabout traffic signs are shown asexamples, including a yield ahead sign in FIG. 5BH, a multi-laneroundabout sign in FIG. 5BI, and two upcoming roundabout signs in FIGS.5BJ and 2BK.

In some embodiments, in-vehicle control computer 150 may detect the curbof the central island of a roundabout at least 10 meters before entrywayof the roundabout. In some embodiments, the in-vehicle control computer150 may detect the apron of the central island.

In some embodiments, the in-vehicle control computer 150 may detect thevehicles that are inside the circle of the roundabout at least apredetermined minimum distance away, e.g., at least 5 meters, 10 meters,or 15 meters away, from the autonomous vehicle 105's entryway of theroundabout. In some embodiments, the in-vehicle control computer 150 maydetect the vehicles that are in the closest entry lane from the leftside and 20 meters away or less from the entryway of the roundabout, atleast a predetermined minimum distance away, e.g., at least 5 meters, 10meters, or 15 meters away, from the autonomous vehicle 105's entryway ofthe roundabout. The in-vehicle control computer 150 may detect thevehicles that are in its exit way if the vehicles are within apredetermined distance, e.g., 10 meters, 15 meters, or 20 meters, fromthe exit way.

When approaching a roundabout, the in-vehicle control computer 150 maydevelop plans according to mapped data and detected data to navigatethrough the roundabout safely. In the process of traversing theroundabout, the autonomous vehicle 105 may slow down its speed forsafety reasons. In some embodiments, the in-vehicle control computer 150may select a predetermined maximum speed, e.g., 3 mph, 5 mph, 8 mph, or10 mph, at a predetermined distance from the entryway of the roundabout,e.g., 8 meters, 10 meters, or 15 meters from the entryway of theroundabout.

For a multi-lane roundabout, the autonomous vehicle 105 may choose thebest entry lane for the intended exit and move toward the exit at leasta predetermined distance, e.g., at least 30 meters, 40 meters, or 50meters, before the entryway of the roundabout. The choice of entry laneis based on the vehicle's destination and planned exit lane. Theautonomous vehicle 105 may follow the guidance provided by traffic signsand/or mapped data and move to the planned lane.

The autonomous vehicle 105 may move to the left entry lane if it isplanning to make a left turn or U-turn. In some embodiments, theautonomous vehicle 105 may move to the right entry lane if it isplanning to make an immediate right turn. In some embodiments, theautonomous vehicle 105 may stay in its current lane if it is planning togo straight.

If there is a vehicle(s) inside the circle of the roundabout on the leftside of the entryway of the autonomous vehicle 105, the autonomousvehicle 105 may yield to the traffic inside the circle and perform afull stop before the entryway and crosswalk. In some embodiments, theautonomous vehicle 105 may yield and fully stop before the entryway andcrosswalk if there is a vehicle(s) in the closest entry lane from theleft side, and less than a predetermined distance from the entryway ofthe roundabout, e.g., 15 meters or less, 20 meters or less, or 25 metersor less.

The autonomous vehicle 105 may fully stop before the entryway andcrosswalk, and yield to pedestrians and bicyclists crossing the roadway.In some embodiments, the autonomous vehicle 105 may fully stop beforethe entryway and crosswalk if its planned exit way of the roundabout isblocked. The in-vehicle control computer 150 of the autonomous vehicle105 may contact the oversight system 350 to seek guidance if the exitway remains blocked for a time equal to or longer than a predeterminedlength of time. The exit way of the roundabout is defined as blocked ifthere is less than a predetermined amount of available space, e.g., 15meters, 20 meters, or 25 meters, in that lane after the crosswalk.

The autonomous vehicle 105 may merge into the roundabout (heading to theright) if the roundabout is available to enter safely. In someembodiments, the in-vehicle control computer 150 predicts successfulmerging into the roundabout based on a predetermined length of time,e.g., 15 seconds required time (X)+5 seconds safety offset time (Y), tocomplete its merge without confronting any other vehicles. If there isno vehicle inside the circle on the left side of the entryway of theautonomous vehicle 105 and there is no vehicle in the closest entry laneon the left side of the autonomous vehicle 105, 20 meters away or lessfrom the entryway of the autonomous vehicle 105, the in-vehicle controlcomputer 150 considers it is safe to merge into the roundabout.

In the embodiments that the autonomous vehicle 105 is a truck, it maytake advantage of law for driving through roundabouts. Some regions havelaws that give right-of-way to semi-trucks and large vehicles inroundabouts. Vehicles are prohibited from driving next to trucks andlarge vehicles inside the circle of the roundabout. If the autonomousvehicle 105 is a truck, it may merge into the roundabout in a way to useand block two lanes in a multi-lane roundabout. This is to ensure thatthe autonomous vehicle 105 has enough space to accommodate its turningpath. By law, vehicles are prohibited from driving next to trucks andlarge vehicles inside the circle of the roundabout.

Once inside a roundabout, the autonomous vehicle 105 may stay in itslane and drive safely inside the circle of the roundabout whilemaintaining a safe distance (curvature corrected following distance)from the front vehicle(s) until it reaches its planned exit. Theautonomous vehicle 105 may move in a counterclockwise direction insidethe circle of the roundabout (or clockwise in some regions depending onthe traffic laws), and can drive at least 5 mph slower than posted speedlimit. In some embodiments, the autonomous vehicle 105 may avoid drivingnext to other vehicles inside the circle of the roundabout, and avoidstopping inside the circle of the roundabout. In some embodiments, theautonomous vehicle 105 may avoid passing other vehicles inside thecircle of the roundabout, and may continue moving around the circle inthe same lane if it misses its planned exit. Posted speed limit insidethe roundabouts is mostly between 15 mph and 25 mph, depending on thelocation and type of the roundabout.

At exit, the autonomous vehicle 105 may exit the roundabout and mergeinto the planned exit way safely. In some embodiments, the autonomousvehicle 105 may abort exiting if there's another vehicle driving inparallel to it on the right side, or if its planned exit way of theroundabout is blocked. If the planned exit way remains blocked after apredetermined number of tries, e.g., 3 tries, 4 tries, or 5 tries, theautonomous vehicle 105 may exit to the next safe exit way and pull oversafely to call the oversight system 350 to seek guidance.

The autonomous vehicle 105 may continue driving around the circle of theroundabout in the same lane to reach the exit way again if exiting isaborted.

The autonomous vehicle 105 may yield to emergency vehicle(s) inside thecircle of roundabout or approaching the roundabout. The autonomousvehicle 105 may fully stop before the entryway and crosswalk if theautonomous vehicle 105 has not entered the roundabout. The autonomousvehicle 105 may exit to its planned exit way, pull over immediately, andyield to emergency vehicle(s) if the autonomous vehicle 105 is alreadyinside the circle of the roundabout when it detects emergency vehicle(s)inside the roundabout or approaching the roundabout.

Two-Way Left Turn Lane

A two-way left-turn lane is a center lane that allows vehicles from eachdirection to make left turn using the same lane. It is permissible tocross the solid yellow line to enter the shared turn lane. Center laneor two-way left turn lane is defined and stored in the digital map. Avehicle may only enter the turning lane close to where it intends toturn. The vehicle shall watch for oncoming vehicles in the lane. Atwo-way left turn center lane is shown in FIG. 5BL.

A two-way left turn (TWLT) lane is indicated by TWLT signs, as shown inFIG. 5BM and FIG. 5BN as examples. The in-vehicle control computer 150may detect two-way left turn lane signs at a predetermined distance,e.g., 200 meters, 222 meters, 250 meters, or 275 meters, when theautonomous vehicle 105 approaches a TWLT sign. The in-vehicle controlcomputer 150 may also detect pavement marking indicating the presence ofa two way left turn lane in the road. FIG. 5BO is a schematic showing atwo-way left turn lane in the middle of the road with pavement markings.If the in-vehicle control computer 150 determines that a detectedtwo-way left turn lane is not mapped, it may inform the oversight system350 in order to update the map.

Before taking a left turn on a TWLT lane, the in-vehicle controlcomputer 150 may identify the entry point in the lane as given in themap at a predetermined distance, e.g., 200 meters, 222 meters, 250meters, or 275 meters, before the autonomous vehicle 105 reaches theplanned turn. In some embodiments, the in-vehicle control computer 150may estimate a possible entry point into the TWLT lane at apredetermined maximum allowed distance, e.g., 250 feet, 300 feet, 350feet, or 400 feet, before the autonomous vehicle 105 reaches the plannedentry into the TWLT lane. The maximal allowed distance may vary for eachregion or may not be applicable. For example, in the state of Californiathis distance is about 200 feet. FIG. 5BP schematically illustrates theestimation of entry point into a TWLT lane.

When approaching the entry point into a TWLT lane, the autonomousvehicle 105 may perform a lane change at least a predetermined distance,e.g., 50 meters, 55 meters, 60 meters, or 65 meters, before theestimated entry point, so that the autonomous vehicle 105 is positionedin the adjacent lane of the TWLT lane. When the autonomous vehicle 105is positioned in the right side of the TWLT lane, the in-vehicle controlcomputer 150 may keep the front and rear left turn signals on. In someembodiments, the in-vehicle control computer 150 may actively detectvehicles which are already in TWLT lane that may be either heading inthe same direction as the autonomous vehicle 105 or driving in oppositedirection. The in-vehicle control computer 150 may detect details of thevehicles driving in opposite direction of the autonomous vehicle 105 anddetermine their speed, position, and sizes.

The in-vehicle control computer 150 may abort the lane change into theTWLT lane if there is not enough lateral or longitudinal space availablefor the autonomous vehicle 105 to merge into the TWLT lane. In thiscase, the autonomous vehicle 105 may continue to move forward in theTWLT lane's right lane, deactivate the left turn signals and inform theoversight system 350. In some embodiments, the in-vehicle controlcomputer 150 may plan an alternate route to continue its mission.

If there is enough space laterally and longitudinally, the in-vehiclecontrol computer 150 may perform a lane change so that the autonomousvehicle 105 is positioned in the TWLT lane. Once entered the TWLT lanethe autonomous vehicle 105 may slow down and come to a stop where thevehicle's front most point reaches the intersecting point of the TWLTlane with the left lane where the autonomous vehicle 105 intends toturn. In some embodiments, the autonomous vehicle 105 may keep its leftturn signal activated during the time when it is in TWLT lane andperforming left turn.

The in-vehicle control computer 150 may detect vehicles in the oppositedirection of the roadway and in the planned TWLT lane at a predetermineddistance, e.g., 200 meters, 222 meters, 250 meters, or 275 meters, fromthe entry point in the TWLT lane.

The in-vehicle control computer 150 may yield to any oncoming vehiclefrom the opposite direction in the roadway while ensuring apredetermined time to collision (TTC), e.g., 6 seconds TTC, 7 secondsTTC, or 8 seconds TTC, before taking left turn. Also, the in-vehiclecontrol computer 150 may yield to any pedestrians crossing the lane thatthe autonomous vehicle 105 plans to turn into, while ensuring apredetermined TTC, e.g., 6 seconds TTC, 7 seconds TTC, or 8 seconds TTC,with oncoming traffic before start taking the turn.

The in-vehicle control computer 150 may estimate whether the lane inwhich it is turning left into is clear so that the rearmost point of theautonomous vehicle 105 can cross the intersection before any oncomingvehicle approaches the intersection. When the path of left turn isclear, the autonomous vehicle 105 may accelerate and adjust its speed tofollow the speed limit of the left lane it is turning into. In case theautonomous vehicle 105 needs to align its position with the left lanethat it intends to turn into or when the in-vehicle control computer 150does not have a clear view of the left lane, the autonomous vehicle 105may creep forward for a predetermined maximum distance, e.g., 2 meters,3 meters, or 4 meters, at a predetermined speed, e.g., 4 mph, 5 mph, or6 mph.

Traffic Light Detection and Response

Traffic lights and their locations are included in the digital mapstored in the memory 175 of the in-vehicle computer 150. In someembodiments, when the autonomous vehicle 105 traverses on a road, thein-vehicle control computer 150 detects traffic light signs at least apredetermined distance away, e.g., 200 meters, 222 meters, 250 meters,or 275 meters away. An example traffic light sign is shown in FIG. 5BQ.The in-vehicle control computer 150 may detect and identify the trafficlight at least a predetermined distance away, e.g., 200 meters, 222meters, 250 meters, or 275 meters away.

When the autonomous vehicle 105 approaches an intersection, thein-vehicle control computer 150 may detect an intersection traffic signat least a predetermined distance away, e.g., 200 meters, 222 meters,250 meters, or 275 meters away. As shown in FIGS. 5BR-5BT as examples,intersection traffic signs may include left-turn-yield-on-green sign,no-turn-on-red sign, no-turn-on-red-except-from-right-lane sign, etc.FIGS. 5BU and 2BV show photos of two more examples of intersectiontraffic signs.

When a traffic light sign is detected, the autonomous vehicle 105 mayslow down by a predetermined percentage of the speed limit, e.g., 8%,10%, or 12%. The in-vehicle control computer 150 may detect a speedlimit sign at least a predetermined length of time in advance, e.g., 8seconds, 10 seconds, or 12 seconds in advance, or a predetermineddistance ahead, e.g., 250 meters, 300 meters, or 350 meters, beforereaching it, whichever distance is greater.

When a red signal light is detected, the autonomous vehicle 105 makes acomplete stop before the vehicle's front bumper reaches the stop lineand no farther than a predetermined distance, e.g., 3 meters, 4 meters,or 5 meters, from the stop line. When a blinking red signal light isdetected, the autonomous vehicle 105 may make a complete stop before thevehicle's front bumper reaches the stop line and no farther than apredetermined distance, e.g., 3 meters, 4 meters, or 5 meters, from thestop line. In some embodiments, the autonomous vehicle 105 may treat theintersection as a stop sign intersection. In some embodiments, when ared arrow signal light is detected, the autonomous vehicle 105 makes acomplete stop before the vehicle's front bumper reaches the stop lineand no farther than a predetermined distance, e.g., 3 meters, 4 meters,or 5 meters, from the stop line.

In some embodiments, when a green arrow signal light is detected, if theautonomous vehicle 105 plans to turn, it may yield to any vehicle,bicycle, or pedestrian still in the intersection. In some embodiments,the autonomous vehicle 105 may proceed and follow the directionindicated by the green arrow. In some embodiments, when solid greensignal light is detected, the autonomous vehicle 105 may proceedcrossing the intersection.

In some embodiments, when a solid yellow signal light is detected, theautonomous vehicle 105 may make a complete stop before the vehicle'sfront bumper reaches the stop line, and no farther than a predetermineddistance, e.g., 3 meters, 4 meters, or 5 meters, from the stop line.When a blinking yellow signal light is detected, the autonomous vehicle105 may slow down by a predetermined percentage of the speed limit,e.g., 8%, 10%, or 12%, of the speed limit. The autonomous vehicle 105may yield to any pedestrians, bicyclists, or vehicles in theintersection.

There are locations that traffic signs are installed together withtraffic light. For example, when a solid green light signal and aleft-turn-yield-on-green sign are detected together, if the autonomousvehicle 105 plans to turn left, it may yield to oncoming traffic beforetaking the left turn. When a solid red signal light and a no-turn-on-redsign are detected together, if the autonomous vehicle 105 plans to turn,it makes a complete stop until the traffic signal changes to greenbefore making the turn. When a solid red signal light and a no-turn-onred-except-from-right-lane sign are detected, as shown in FIG. 5BT forthe traffic sign, if the autonomous vehicle 105 is in the right lane andplans to turn right, it may make a complete stop before proceeding tomake the right turn.

Intersection Navigation

The digital map stored in the memory 175 of the in-vehicle controlcomputer 150 intends to store data for different types of intersections,including their locations and details of their types. The types ofintersections include four-way intersection, T-junction, Y-intersection,traffic circle intersection, fork intersection, controlled intersection,uncontrolled intersection, and pedestrian crosswalk intersection. Basedon behavior, intersections can be categorized as controlled intersectionand uncontrolled intersections. Besides retrieved intersection data fromthe map, the in-vehicle control computer 150 may actively inspect andidentify different intersection types when it is on road. By definition,a controlled intersection employs stop signs, traffic signals oremergency personnel, and an uncontrolled intersection is a roadintersection with no traffic light or road signs to indicate theright-of-way.

An intersection can also be classified as a stop sign intersection, atraffic signal intersection, a yield intersection, an intersectionallowing U-turn, an intersection with turning lanes, and etc.

In some embodiments, when approaching an intersection, the in-vehiclecontrol computer 150 may detect different moving objects such aspedestrians, cyclists, other vehicles, and emergency vehicles at apredetermined minimum distance, e.g., 200 meters, 222 meters, 250meters, 275 meters, or 300 meters, from the autonomous vehicle 105. Whena moving object is detected when the autonomous vehicle 105 isapproaching an intersection, it may come to a complete stop at apredetermined distance, e.g., 3 meters, 4 meters, or 5 meters away fromthe object.

When a crosswalk is detected, the autonomous vehicle 105 may stop withthe vehicle's front most point behind the crosswalk line but no furtherthan a predetermined distance, e.g., 3 meters, 4 meters, or 5 meters,from the crosswalk line.

Roundabouts or traffic circles are designed with a truck apron asdescribed in a previous section. A truck apron is a raised section ofconcrete around the central island of a roundabout that acts as an extralane for large vehicles and vehicles with trailers. The back wheels ofan oversized vehicle can ride up on the truck apron so that it may moreeasily complete the turn, while the raised portion of concretediscourages smaller vehicles from using it. A truck apron is shown inFIG. 5BY as an example.

Before entering a roundabout or traffic circle, the autonomous vehicle105 may slow down to below a predetermined speed, e.g., 15 mph, 20 mph,25 mph. At the traffic circle, the autonomous vehicle 105 may stop andwait up to a predetermined length of time, e.g., 4 seconds, 5 seconds,or 6 seconds, before entering in the circulating traffic. Vehiclesalready inside the roundabout have the right of way. If other vehicleshave not attempted to proceed through the intersection within that time,the autonomous vehicle 105 may proceed through vehicles with the rightof way. FIGS. 5BZ and 2CA schematically shows examples of roundaboutsand traffic flows. When the autonomous vehicle 105 enters and exits aroundabout, if the in-vehicle control computer 150 detects that acrosswalk is present at the roundabout, it may yield to the pedestriansusing the crosswalk.

When the autonomous vehicle 105 approaches a traffic circle and detectsthat an emergency vehicle is in the roundabout, the autonomous vehicle105 may enter the traffic circle after the emergency vehicle has exited.In some embodiments, when the autonomous vehicle 105 encounters anemergency vehicle when it is in the roundabout, it may continue to exitand then move to the right side to stop.

A fork intersection is an intersection where one road splits intomultiple roads. For example, one lane of the main road divides into twomini-lanes with one mini-lane connecting to a joining road and the othermini-lane continuing along the road's original path. Typically, at afork intersection there is a median area separating the two dividingroads of the main roadway. An example fork intersection is shown in FIG.5CB.

At an uncontrolled intersection, the autonomous vehicle 105 may yieldthe right of way to any vehicles that are traveling in lanes notrequired to stop and with paths intersecting the vehicle's planned path.In some regions, such as in the state of Arizona, the autonomous vehicle105 may treat any unregulated intersection as an all way stopintersection and come to a complete stop before going through. Also,when making left or right turn, turn into the closest lane, as shown inFIG. 5CC.

At an intersection, the in-vehicle control computer 150 may activelydetect any non-compliant driver at a predetermined distance, e.g., 100meters, 125 meters, 150 meters, 175 meters, or 200 meters, beforereaching the intersection. When a non-complaint driver is detected at anintersection, in order to avoid a collision, the autonomous vehicle 105may yield until the non-complaint driver has cleared the intersection.

At a four-way intersection, the autonomous vehicle 105 may proceedthrough the intersection if any through traffic vehicle comes to acomplete stop in order to allow vehicle to transition to its targetlane. A four-way intersection is schematically shown in FIG. 5CD.

Crosswalks

The digital map stored in the memory 175 of the in-vehicle controlcomputer 150 intends to include information and location for allcrosswalks. But the in-vehicle control computer 150 may detect crosswalksigns at least a predetermined distance, e.g., 200 meters, 222 meters,250 meters, or 275 meters, from the crosswalk. FIGS. 5CE-5CM showexamples of crosswalk traffic signs. When a conflict is detected betweenmapped information and perceived crosswalk information, the in-vehiclecontrol computer 150 may inform the oversight system 350 and take theperceived information as higher priority. The in-vehicle controlcomputer 150 may also inform the oversight system 350 if a non-mappedcrosswalk sign or crosswalk road marking is detected in order to updatethe map.

When crosswalk markings or crosswalk sign is detected, the autonomousvehicle 105 may slow down to a predetermined speed, e.g., 15 mph, 20mph, or 25 mph. In some embodiments, when crosswalk markings and stoptraffic sign are detected, the autonomous vehicle 105 may stop no closerthan a predetermined distance, e.g., 0.5 meter, 1 meter, or 1.5 meter,from the crosswalk line and wait until no pedestrians are in thecrosswalk before proceeding to cross the crosswalk. When crosswalkmarkings and red traffic light are detected, the autonomous vehicle 105stops no closer than a predetermined distance, e.g., 0.5 meter, 1 meter,or 1.5 meter, from the crosswalk line and wait until there nopedestrians in the crosswalk before proceeding to cross the crosswalk.Similarly, when crosswalk markings and yield traffic sign are detected,the autonomous vehicle 105 stops no closer than a predetermineddistance, e.g., 0.5 meter, 1 meter, or 1.5 meter, from the crosswalkline and wait until there no pedestrians in the crosswalk beforeproceeding to cross the crosswalk.

Dynamic Zone Detection and Response

A dynamic zone is defined as a zone where traffic patterns vary onperiodic basis. For example, there may be periodic lane closures andopenings based on traffic volume or time of the day. FIG. 5CN shows lanearrangements in a two-way road at different times of a day. On the leftside of FIG. 5CN, there are 2 lanes to the right and 2 lanes to theleft. But at a different time of the same day, as shown on the rightside of FIG. 5CN, the lane arrangement is converted to 3 lanes to theright and 1 lane to the left.

As the autonomous vehicle 105 travels on a road and detects the newestchanges, the digital map stored in the memory 175 of the in-vehiclecontrol computer 150 may continuously or actively update the latestinformation about dynamic zones, including location and closure timinginformation. FIG. 5CO schematically shows another example of lanechanges at an intersection.

The in-vehicle control computer 150 may detect the dynamic zone changesigns in lane or on shoulder at a distance greater than the decelerationdistance. In some embodiments, the in-vehicle control computer 150 maydetect the lane boundaries in the dynamic zone. A lane may be definedwithin the left and the right boundaries defined by yellow road marking,white lines, road edges, LED lights, barricades, and etc. FIGS. 5CP-2CTshow examples of lane changing and boundary marking.

The in-vehicle control computer 150 may consider the dynamic changes inlanes, taking into account the lane boundaries. When it comes to makingdecisions around changing lanes, the in-vehicle control computer 150 mayprioritize safety over regulation and over efficiency. The in-vehiclecontrol computer 150 may categorize lane change intentions based onsafety, regulatory, and efficiency concerns, unless otherwise specified.In some embodiments, the priority order may be as follows from thehighest priority to the lowest priority, critical safety, non-criticalsafety, regulatory, efficiency, precautionary, and preference. Forexample, in FIG. 5CU is a photo showing four lanes available to drive inthe direction and two lanes prohibiting entrance. In some embodiments,the in-vehicle control computer 150 may plan a lane selection among thedrivable lanes. FIG. 5CV also shows multiple lanes, but the lanes thein-vehicle control computer 150 may choose are limited by the dynamicbarrier.

In some embodiments, the autonomous vehicle 105 is a truck. Thein-vehicle control computer 150 evaluates if the available lanes permittrucks to complete the route, based on dynamic zone signs. Thein-vehicle control computer 150 may consider the type of cargo, load andbraking capabilities it has before planning a lane change.

The in-vehicle control computer 150 may check if the available lane isfor passing vehicles in front or can be used to continue the plannedmission.

When changing lane in a dynamic zone, the autonomous vehicle 150 mayperform lane change according to regulatory lane change intentionfollowing the priority model defined.

Fixed Zones

The map defines fixed zones as areas or roads within a predetermineddistance, e.g., 100 meters, 200 meters, or 300 meters, from a hospital,a fire station, or an airport. In these fixed zones, there is a higherprobability to encounter special vehicles, e.g., emergency vehicles.

The in-vehicle control computer 150 on the autonomous vehicle 105detects a fixed zone before entering it. In some embodiments, thein-vehicle control computer 150 tries to identify the fixed zones byretrieving data from the stored map. In some embodiments, the in-vehiclecontrol computer 150 detects the traffic signs relevant to the fixedzones.

An emergency-signal-ahead sign is the most common sign that is used toalert road users to fixed zones, i.e., locations where unexpectedentries into the roadway by emergency vehicles may occur. Theemergency-signal-ahead sign is typically placed below an emergencyvehicle sign. The emergency vehicle sign with the emergency-signal-aheadsign supplemental plaque may be placed in front of all emergency-vehicletraffic control signals. The emergency vehicle sign, or a word messagesign indicating the type of emergency vehicle, Such as a rescue squad,may be used in front of an emergency-vehicle station when noemergency-vehicle traffic control signal is present. An emergencymedical services symbol sign may be used to identify medical servicefacilities that are included in the emergency medical services system.The emergency medical service symbol sign may be used above a hospitalsign or a hospital symbol sign or above a sign with a legend ambulancestation or an emergency-medical-care sign. The in-vehicle controlcomputer 150 may use these signs to detect a hospital zone as one of thefixed zones.

Guide signs for commercial service airports and non-carrier airports maybe installed in interstate freeways, other freeways, or conventionalhighway intersections connected to an airport, normally less than 15miles from the airport. The airport symbol sign along with asupplemental plaque may be used to indicate the specific name of theairport. An airport symbol sign, with or without a supplemental nameplaque or the word airport together with an arrow may be used as atrailblazer. Adequate trailblazer signs may be in place prior toinstalling the airport guide signs.

Various types of fixed zone signs are shown in FIGS. 5CW-5BD asexamples.

The autonomous vehicle 105 may navigate through and pass fixed zonesautonomously and safely.

In a hospital fixed zone, the autonomous vehicle 105 may drive at apredetermined speed that is lower than the posted speed limit for thearea, e.g., 5 mph, 10 mph, or 15 mph below the posted speed limit. Theautonomous vehicle 105 may drive in the right lane and be ready topull-over once an emergency vehicle is present. The autonomous vehicle105 can avoid using its horn in hospital zones.

In a fire station fixed zone, the autonomous vehicle 105 may drive at apredetermined speed that is lower than posted speed limit for the area,e.g., 10 mph, 15 mph, 20 mph, or 25 mph below the posted speed limit.The autonomous vehicle 105 may drive in the right lane and be ready topull-over if an emergency vehicle is present.

In an airport fixed zone, the autonomous vehicle 105 may avoid drivingin the exit lanes.

High Luminosity Change

High luminosity change is defined in the digital map that is stored inthe memory 175 of the in-vehicle control computer 150 as a sudden changein luminosity by a predetermined amount, e.g., by over 75 times, over100 times, or over 125 times.

High luminosity change, or the sudden change in luminosity may happen ina few different ways, for example, in light transition areas, from sunglare, or from oncoming traffic headlights.

The map is developed with the intention to include locations of lighttransition areas, including when exiting from a lighted street or roadand moving to a dark street or road, or the opposite, when entering atunnel, and when exiting a tunnel. FIG. 5DC shows a lighted street, andFIG. 5DD shows a lighted tunnel.

The in-vehicle control computer 150 actively detects light transitionareas from a predetermined distance away, e.g., from 100 meters, 150meters, 175 meters, 200 meters, or 225 meters away.

In addition to light transition areas, the in-vehicle control computer150 also actively detect high luminosity change.

Grade Difference Merge

Entry ramp (e.g., on-ramp) and exit ramp (e.g., off-ramp) are ramps thatallow vehicles to enter or to exit a roadway or to change for oneroadway to another. The map stored in the memory 175 of the in-vehiclecontrol computer 150 intends to keep entry and exit ramp information,including ramp type, e.g., entry or exit, ramp class, the remainingdistance to the ramp, the maximum ramp speed, and etc.

The in-vehicle control computer 150 considers entry ramp classes asparallel acceleration lane or tapered acceleration lane. The last partof a parallel lane entry is parallel to traffic on the roadway thevehicle intends to enter. This part of the ramp is also called anacceleration lane. On the other hand, a tapered lane entry doesn't havea parallel part to the traffic. Example entry ramps are schematicallyillustrated in FIG. 5DE and FIG. 5DF.

The in-vehicle control computer 150 may consider exit ramp classes asparallel deceleration lane, tapered deceleration lane, or exit onlylane. FIGS. 5DG-5DI schematically illustrate three example exit ramps.

The map stored on the in-vehicle control computer 150 also intends tokeep information about the presence of a cloverleaf ramp and itsattributes, e.g., the remaining distance to the ramp, the maximum rampspeed, the length of the white line break for the ramp, and etc. Acloverleaf ramp is a kind of entry and exit ramp in one lane. As shownin FIG. 5DJ as an example, a cloverleaf ramp is an entry ramp from thebottom side of the figure, and it evolves to an exit ramp when reachingat the top. A cloverleaf ramp is also called a K-ramp.

The in-vehicle control computer 150 of the autonomous vehicle 105actively detects road markings and traffic signs to identify ramps andtheir classes. A ramp traffic sign is shown in FIG. 5DK as an example.In the embodiments that the in-vehicle control computer 150 detects anunmapped ramp or absence of mapped ramp, the in-vehicle control computer150 may inform the oversight system 350 to update the map.

In some embodiments, when a ramp is identified, the in-vehicle controlcomputer 150 develops actions according to pre-developed plans. When theautonomous vehicle 105 is in the right lane on a highway approaching atapered acceleration lane entry ramp or a parallel acceleration laneentry ramp, the in-vehicle control computer 150 may perform anon-critical lane change the lane to the left, if possible, to avoid anyvehicle from the entry ramp. If the autonomous vehicle 105 is in a lanethat an oncoming vehicle from an entry ramp is merging into and thevehicle's bumper is head of the autonomous vehicle 105, the in-vehiclecontrol computer 150 may dynamically adjust the autonomous vehicle 105'sspeed to allow the vehicle from the entry ramp to merge into the lane.

If the autonomous vehicle 105 is in a tapered lane entry ramp, thein-vehicle control computer 150 may yield to a vehicle in the lane wherethe autonomous vehicle 105 is merging into when the vehicle's bumper isahead of the bumper of the autonomous vehicle 105. In some embodiments,if the autonomous vehicle 105 is in a tapered lane entry ramp, it maystop if the time to collision (TTC) is less than a predetermined lengthof time, e.g., 5 seconds, 6 seconds, 7 seconds, 8 seconds, or 9 seconds,with a vehicle in the roadway lane where the autonomous vehicle 105 ismerging into.

When another vehicle is in a parallel lane entry ramp and is trying tomerge into a lane that the autonomous vehicle 105 is in before the endof the acceleration lane, if that vehicle's bumper is ahead of theautonomous vehicle 105's bumper, the in-vehicle control computer 150 maydynamically adjust its speed to allow the other vehicle from the entryramp to merge into the lane. The in-vehicle control computer 150 mayestimate suitable gap available behind the vehicle before merging. Thein-vehicle control computer 150 may merge with the vehicle before theend of acceleration lane but after being in parallel with the it. SeeFIG. 5DL which schematically shows an example.

When the autonomous vehicle 105 is in a zipper merge lane, aftercrossing the ramp meter lights, the in-vehicle control computer 150 mayadjust its speed, while respecting the ramp speed limit, in the rampmeter lane so that it can find sufficient lateral gap to merge into thefreeway lane. Getting sufficient lateral gap allows the autonomousvehicle 105 to merge in the intended lane and yield to other vehicleswhose bumper is ahead of the autonomous vehicle 105's.

In some embodiments, the autonomous vehicle 105 may activate its turnlights according to the on or off ramp position when it is entering intoor exiting from the lane.

When the autonomous vehicle 105 is in an exit ramp lane but does notintend to take the exit ramp, the in-vehicle control computer 150 maydynamically adjust the speed to make a non-critical lane change at leasta predetermined distance, e.g., 75 meters, 100 meters, or 125 meters,ahead of exit ramp. In other words, the autonomous vehicle 105 maychange the lane if its current lane is an exit only lane.

In some embodiments, if the autonomous vehicle 105 intends to take anexit ramp, the in-vehicle control computer 150 may plan to get into thelane that merges into the deceleration lane or the exit lane of the exitramp. The autonomous vehicle 105 may enter in the deceleration lane atthe beginning. FIG. 5DM schematically shows an example of taking an exitramp. When the exit ramp is a tapered or parallel exit ramp, thein-vehicle control computer 150 may plan to decelerate to enter the exitramp as indicated in mapped data or detected traffic sign.

When the autonomous vehicle 105 goes through an overleaf ramp section,the in-vehicle control computer 150 may identify whether a vehiclecoming from the adjacent lane intends to merge in the lane theautonomous vehicle 105 is current lane. The autonomous vehicle 105 mayyield if the bumper of the oncoming vehicle in that lane is ahead of theautonomous vehicle 105's bumper. In case the autonomous vehicle 105intends to take the next overleaf exit ramp, it may make a non-criticallane change to take the lane that merges into overleaf exit ramp.

The autonomous vehicle 105 may detect if the turn lights of the vehiclesin adjacent overleaf entry ramp are activated to merge in current laneof the autonomous vehicle 105.

When the autonomous vehicle 105 is in an overleaf entry ramp, thein-vehicle control computer 150 may activate turn lights and dynamicallyadjust the speed to find sufficient lateral gap to make a non-criticallane change to continue its mission. If the autonomous vehicle 105intends to take the overleaf exit ramp, it may continue to drive on thelane that merge in exit ramp lane.

When the autonomous vehicle 105 approaches a curved ramp, the in-vehiclecontrol computer 150 may retrieve information about the curved ramp fromthe map, including information about curvature, curvature angle andgradient of the curved ramp. The in-vehicle control computer 150 mayplan to slow down to enter the curved ramp lane at a speed that is lowerthan the speed limit by a predetermined percentage, e.g., 10%, 15%lower, or 20% lower. And the in-vehicle control computer 150 may adjustthe speed dynamically to have lateral acceleration no more than apredetermined value, e.g., 1.5 m/s², 2 m/s², or 2.5 m/s².

Example Technique for Operating an Autonomous Vehicle on a Roadway witha Grade

One objective of this disclosure includes controlling an autonomousvehicle 105 on the roadway has a grade. FIG. 5DN illustrates an examplemethod which can be used to control the autonomous vehicle 105 based onthe detected roadway data and mapped data. The method 550 may bedescribed herein as being performed by one or more processors, which mayinclude the in-vehicle control computer 150.

The method 550 begins at block 552. At block 554, the in-vehicle controlcomputer 150 is configured to receive detected roadway conditions dataincluding roadway grade data from the at least one perception sensor onthe autonomous vehicle 105.

At block 556, the in-vehicle control computer 150 is configured toretrieve the mapped data having roadway grade data from thenon-transitory computer readable medium on the in-vehicle controlcomputer of the autonomous vehicle 105.

At block 558, the in-vehicle control computer 150 is configured todetermine that the roadway has a grade based on the detected roadwaygrade data and the retrieved mapped roadway grade data.

At block 560, in response to the determining that the roadway has agrade, the in-vehicle control computer 150 is configured to determinethat the grade of the roadway is greater than or equal to apredetermined high grade value and less than a predetermined gradelimit.

At block 562, in response to the determining that the grade of theroadway is greater than or equal to a predetermined high grade value andless than a predetermined grade limit, the in-vehicle control computer150 is configured to operate the autonomous vehicle to change its laneto the right-most lane. Then the method comes to an end 564.

Environmental Conditions

The environmental conditions under which an autonomous vehicle 105 isoperating can greatly affect how safe certain maneuvers will be duringnavigation. Thus, it is desirable for the autonomous vehicle 105 toobtain accurate information regarding the current and/or futureenvironmental conditions which the autonomous vehicle 105 may encounterwhile traversing a route.

Environmental conditions, such as inclement weather, may include variousenvironmental conditions that can negatively affect the ability of theautonomous vehicle 105 to continue safely navigating along its definedroute. When the environmental conditions are sufficiently severe, theautonomous vehicle 105 may not be able to safely continue navigation. Insuch conditions, the autonomous vehicle 105 can determine to pull overto the side of the roadway or otherwise cease navigation for the safetyof the autonomous vehicle 105 as well as other entities on or near theroadway. In the particular embodiment of an autonomous truck 105, it cantake a significant amount of time to perform maneuvers such as stopping,changing lanes, and swerving to avoid obstacles during different typesof inclement weather or other environmental conditions. Thus, it isdesirable for the autonomous vehicle 105 to be able to react to changingenvironmental conditions before the conditions become severe enough suchthat the autonomous vehicle 105 determines to cease continued navigationfor safety.

There are a number of aspects related to the autonomous vehicle's 105ability to sense and respond to environmental conditions, including thedetection of inclement conditions, operation of the autonomous vehicle105 when outside of an ODD, detection of icy roads, response to roadtraction conditions, and operational limits of the autonomous vehicle105 to road water conditions.

In certain embodiments, the in-vehicle control computer 150 of theautonomous vehicle 105 described herein can address at least some of theabove described problems by receiving perception data from at least oneperception sensor of the autonomous vehicle 105, receiving an indicationof current weather conditions from an external weather condition source,determining a current environmental condition severity level from aplurality of severity levels based on the perception data and theindication of current weather conditions, modifying one or more drivingparameters that govern a range of actions that can be autonomouslyexecuted by the autonomous vehicle 105, and navigating the autonomousvehicle 105 based on the modified driving parameters. The range ofactions can include any actions that the autonomous vehicle 105 may takein response to the determined environmental condition severity level,including an MRC maneuver, slowing down, activating headlights, avoidinglane changes, avoiding lane biasing, etc.

Inclement Weather

Inclement weather may include various environmental conditions typicallycaused by weather conditions that can negatively affect the ability ofthe autonomous vehicle 105 to continue safely navigating along itsdefined route. In certain embodiments, the in-vehicle control computer150 can be configured to receive information from a variety of sourcesincluding the vehicle sensor subsystems 144 and the oversight system 350to determine whether the environmental conditions are indicative ofinclement weather than may affect the ability of the autonomous vehicle105 to continue along its current or defined route. In some embodiments,the in-vehicle control computer 150 can receive a periodically updatedmap that includes the latest weather information and/or warning, forexample, from the oversight system 350. In some embodiments, theperiodically updated map may be continuously updated to include thelatest weather information and/or warnings. FIGS. 6A and 6B illustrateexample visualizations of the periodically updated map in accordancewith aspects of this disclosure.

In some embodiments, the in-vehicle control computer 150 can beconfigured to determine which of a plurality of levels of severitycorresponds to the environmental conditions. For example, the in-vehiclecontrol computer 150 can be configured to assign the environmentalconditions to one of the following levels of severity: normal, degraded,cautionary, and critical. However, aspects of this disclosure are notlimited thereto and the in-vehicle control computer 150 can beconfigured to assign the detected environmental conditions to a greateror fewer number of severity levels.

As used herein, a normal weather condition (e.g., a normal environmentalseverity) may generally refer to weather or environmental conditionsthat do not impair any of the autonomous vehicle's 105 Ego's visibility,traction, and stability levels.

As used herein, a degraded weather condition (e.g., a degradedenvironmental severity) may generally refer to any inclement weather orenvironmental conditions that contain a degraded visibility level, adegraded traction level, and/or a degraded stability level.

As used herein, a cautionary weather condition (e.g., a cautionaryenvironmental severity) may generally refer to any inclement weather orenvironmental conditions that contain a cautionary visibility level, acautionary traction level, and/or a cautionary stability level.

As used herein, a critical weather condition (e.g., a criticalenvironmental severity) may generally refer to any inclement weather orenvironmental conditions that are out of ODD and are no longer safe forthe autonomous vehicle 105 to continue operations. The autonomousvehicle 105 may receive a warning indicating a critical weathercondition by, for example, the US weather service.

In some embodiments, the in-vehicle control computer 150 can receive anindication of a severe and/or critical environmental condition from anexternal source, such as the United States Weather Service or otherexternal source. As used herein, wireless emergency alerts (WEA) maygenerally refer to emergency messages sent by an authorized governmentalerting authorities through the autonomous vehicle's 105 networkcommunications subsystem 178.

In certain embodiments, the in-vehicle control computer 150 can beconfigured to execute an MRC maneuver as soon as possible in response toreceiving an indication that the environmental conditions are criticalfrom the external source. Examples of critical environmental conditionsinclude tornados and other extreme weather events where no vehiclesshould be operating except for rescue workers.

In response to determining that the autonomous vehicle 105 shouldexecute an MRC maneuver, the in-vehicle control computer 150 can beconfigured to perform a first MRC maneuver (e.g., pulling over to ashoulder of the roadway). The first MRC maneuver may be safer than otherMRC maneuvers for both the autonomous vehicle 105 and other entities onor near the roadway.

In some embodiments, in response to receiving an indication of acritical environmental condition from the external source, thein-vehicle control computer 150 can be configured to prepare to executean MRC maneuver and inform the oversight system 350 to request a finaldecision. The received critical environmental condition can indicatethat the weather condition is occurring within a defined radius of theautonomous vehicle 105. The oversight system 350 may make a finaldecision regarding any actions to be taken by the autonomous vehicle 105in response to the received critical environmental condition.

The in-vehicle control computer 150 can also be configured to assign aseparate level of severity to each of a plurality of driving conditionsincluding one or more of the following: road traction, visibility, andstability. The in-vehicle control computer 150 can determine an overallenvironmental condition severity level based on a combination of thelevels of severity for each of the driving conditions. In someembodiments, the memory 175 can store a matrix that relates eachcombination of the levels of severity for each of the driving conditionsto a corresponding overall environmental condition severity level. Thatis, each combination of the levels of severity for each of the drivingconditions may have a different cumulative effect on the ability of theautonomous vehicle 105 to continue safely navigation, and thus, thematrix can provide an accurate assessment of the severity of the currentenvironmental conditions/inclement weather.

In some embodiments, a degraded stability level can be measured bydetecting an uncontrolled deviation from the center of the autonomousvehicle's 105 lane at a standard bias level. In some embodiments, adegraded stability level can generally refer to a degraded weathercondition that causes impairment to the autonomous vehicle's 105stability due to inclement weather where the autonomous vehicle 105 isstill within ODD.

The in-vehicle control computer 150 can also be configured to modify thebehavior and speed of the autonomous vehicle 105 to respond to inclementweather that contains degraded stability levels. In some embodiments,the in-vehicle control computer 150 is configured to receive weatherreports from the oversight system 350. The in-vehicle control computer150 can also detect present weather conditions via one or more of thesensors of the vehicle sensor subsystems 144.

During a degraded stability level, in certain embodiments, thein-vehicle control computer 150 can be configured to cause theautonomous vehicles 105 to perform one or more of the following: lanebiasing, MRC maneuvers, and/or a lane change.

The in-vehicle control computer 150 can also detect a degraded stabilitylevel in response to detecting one or more of the following conditions:changes in wind conditions, and degradation of the autonomous vehicle's105 stability level due to a change in weather.

In response to detecting a degraded stability level, the in-vehiclecontrol computer 150 can be configured to slow the autonomous vehicle105 to a safer speed, and avoid lane changes and lane biases unlesseither one is necessary. Under degraded stability levels, the in-vehiclecontrol computer 150 can be configured to, if safe to do so, lane changeto the right-most lane and inform the oversight system 350 of thedegraded weather response.

In one example scenario, the in-vehicle control computer 150 can beconfigured to detect a change in stability that dictates a degradedweather response. In response, the in-vehicle control computer 150 canbe configured to reduce the speed of the autonomous vehicle 105 toensure a stopping distance with the maximum available deceleration ratealways remains available. The in-vehicle control computer 150 can beconfigured to refrain from making any lane changes except with criticalintent. The in-vehicle control computer 150 can be configured to refrainfrom lane biasing the autonomous vehicle 105 except to avoid collisions.The in-vehicle control computer 150 can be configured to lane change tothe right-most lane (or left-most lane when in a left-hand driveregion), if it is safe to do so.

The in-vehicle control computer 150 can be configured to detect acautionary stability level by measuring an uncontrolled deviation fromthe center of the autonomous vehicle's 105 lane at a maximum bias withinthe lane level. As used herein, a cautionary stability level generallyrefers to a cautionary weather condition or environmental condition thatcauses impairment to the autonomous vehicle's 105 stability due toinclement weather and is at risk of becoming out of ODD.

The in-vehicle control computer 150 can be configured to detect acautionary stability level in response to the autonomous vehicle 105modifying its behavior and speed to response to inclement weather thatcontains cautionary stability levels. In particular, in-vehicle controlcomputer 150 can be configured to detect a cautionary stability level inresponse to one or more of the following conditions: detecting a changein weather conditions, the autonomous vehicle's 105 stability level isimpaired to cautionary levels as driven by a change in weather that mayplace the autonomous vehicle 105 in an out-of-ODD situation.

During a cautionary stability level, the in-vehicle control computer 150can be configured to lane bias, perform an MRC maneuver, and/or performa lane change.

In response to detecting a cautionary stability level, the in-vehiclecontrol computer 150 can be configured to perform one or more of thefollowing actions: slow the autonomous vehicle 105 to safer speeds,avoids lane changes and lane biases unless either are absolutelynecessary, and lane change to the right-most lane.

In one example scenario, the in-vehicle control computer 150 can beconfigured to detect a change in stability that dictates a cautionaryweather response. In response, the in-vehicle control computer 150 canbe configured to execute lane change maneuvers to the right-most lanefor the duration of any cautionary weather. The in-vehicle controlcomputer 150 can also be configured to activates emergency lights untilthe cautionary weather conditions have concluded. The in-vehicle controlcomputer 150 can be configured to avoids all lane changes, except forthose that are required for safety (e.g., lane change to the right-mostlane) or those required to continue the current mission (e.g., to takean exit to stay on route). If the autonomous vehicle 105 must lanechange, the in-vehicle control computer 150 can be configured to use acritical lane change. The in-vehicle control computer 150 can further beconfigured to use lane biases to avoid collision.

If the in-vehicle control computer 150 detects degraded visibility anddegraded traction level simultaneously, the in-vehicle control computer150 can be configured to reduce the speed of the autonomous vehicle 105to ensure a stopping distance with the maximum available decelerationrate remains available. In response to detecting degraded stabilitylevels or degraded traction levels, the in-vehicle control computer 150is configured to refrain from any lane changes except when with criticalintent. In response to detecting degraded visibility levels, thein-vehicle control computer 150 can refrain from making any changesexcept for with critical intent if a target back critical distancebehavior can be confirmed. In some embodiments, confirmation of thetarget back critical distance behavior can include detecting andlocalizing vehicles behind the autonomous vehicle 105 that are in atarget lane that the autonomous vehicle 105 is intending to lane changeor maneuver into. These detected vehicles may correspond to the vehiclesthat autonomous vehicle 105 we will be cutting in front of if the lanechange is executed. The in-vehicle control computer 150 can beconfigured to verify that the detected vehicles meet one or moreconditions for distance and relative velocity in order for theautonomous vehicle 105 to perform the lane change. When the in-vehiclecontrol computer 150 has determined that there is decreased visibility(e.g., detecting degraded visibility levels), the in-vehicle controlcomputer 150 can determine whether it would be safe to make the lanechange if a vehicle is at the threshold of the rear visibility of theautonomous vehicle 105 and the vehicle is driving the speed limit. Inresponse to determining that it would be safe to make a lane changeunder these conditions, the autonomous vehicle 105 can control theautonomous vehicle 105 to execute the lane change.

If the in-vehicle control computer 150 detects degraded traction levels,the in-vehicle control computer 150 can be configured to apply themaximum available deceleration rate or less to decelerate. If thein-vehicle control computer 150 detects degraded traction levels ordegraded stability, the in-vehicle control computer 150 can beconfigured to lane bias to avoid collision. If safe to do so, thein-vehicle control computer 150 can be configured to lane change to theright-most lane.

The in-vehicle control computer 150 can also be configured to perform aset of standard actions in response to detecting any type of degradedenvironmental conditions. For example, the in-vehicle control computer150 can be configured to: execute lane change maneuvers to theright-most lane for the duration of any inclement weather, activate theautonomous vehicle's 105 emergency lights until the cautionary weatherconditions have concluded, avoid all lane changes, except for those thatare required for safety (e.g., lane change to the right-most lane) orthose required to continue the autonomous vehicle's 105 mission (e.g.,to take an exit), use a critical lane change if a lane change isrequired, and apply the maximum available deceleration rate or less todecelerate.

The in-vehicle control computer 150 can also be configured to perform aset of standard actions in response to detecting any type of criticalenvironmental conditions. For example, the in-vehicle control computer150 can be configured to, in response to detecting a sudden and extreme(as defined by the US weather service) change in weather that isdetermined to be out-of-ODD (e.g., sudden tornado) or in response toreceiving a US Weather service Bulletin warning of imminent extremeweather in ego's immediate path, the in-vehicle control computer 150 canactivate its emergency lights, determine the proper MRC maneuver toconduct, execute the proper MRC maneuver, and enter a safe state toprotect itself during the extreme weather event and keep hazard lightson for visibility.

The in-vehicle control computer 150 can also be configured to perform aset of standard actions in response to detecting both traction andstability cautionary environmental conditions. For example, thein-vehicle control computer 150 can detect a change in traction andstability that dictates a cautionary weather response. In response, thein-vehicle control computer 150 can perform one or more of the followingactions: execute lane change maneuvers to the right-most lane for theduration of any cautionary weather, activates the autonomous vehicle's105 emergency lights until the cautionary weather conditions haveconcluded, reduces the autonomous vehicle's 105 speed to ensure astopping distance with maximum available deceleration rate alwaysremains available, avoids all lane changes, except for those that arerequired for safety (e.g., lane change to the right-most lane) or thoserequired to continue the autonomous vehicle's mission (e.g., to take anexit), if the autonomous vehicle must lane change, the in-vehiclecontrol computer 150 can use critical lane change, the autonomousvehicle 105 applies the maximum available deceleration rate or less todecelerate, and lane biases to avoid collision.

The in-vehicle control computer 150 can also be configured to providesome or all of the data collected regarding the environmental conditionsto the oversight system 350, for example, via the network communicationssubsystem 178. In some embodiments, the in-vehicle control computer 150can provide the level of severity determined for each of the drivingconditions as well as the corresponding location. The oversight system350 may update the continuously updated map in part based on theinformation regarding the environmental conditions received from one ormore autonomous vehicles 105 in a fleet.

One example driving condition that the in-vehicle control computer 150can track is the severity level of road traction. In some embodiments,the in-vehicle control computer 150 can detect the road traction levelbased on the current weather and/or the current temperature. Inaddition, the continuously updated map can also include datarepresentative of the road traction for at least a portion of theroadways in the map. Additional details regarding the detection andevaluation of road traction are provided in the road tractionsubsection. In some embodiments, the autonomous vehicle 105 isconfigured to operate autonomously when the ambient air temperature isbetween −12 degrees Celsius and 40 degrees Celsius.

Another example driving condition that the in-vehicle control computer150 can track is the severity level of visibility. In particular, thein-vehicle control computer 150 can determine the visibility distanceahead of the autonomous vehicle 105, for example, based on imagesobtained using a camera and/or other sensors of the vehicle sensorsubsystems 144.

The in-vehicle control computer 150 can control the autonomous vehicle105 to adjust its speed so as to maintain a time to collision (TTC)(e.g., the amount of time until the autonomous vehicle 105 reaches thecurrent visibility distance) that is greater than a threshold time. Inone example, the threshold time may be at least six seconds, however,the threshold time may be longer or shorter depending on the particularimplementation.

Example environmental conditions that can affect visibility include snowand/or fog. Thus, the in-vehicle control computer 150 can determinewhether the current environmental conditions are indicative of snow orfog based on: the continuously updated map and/or data received from oneor more of the sensors of the vehicle sensor subsystems 144. In responseto determining that the autonomous vehicle 105 is experiencingenvironmental conditions that can affect visibility, the in-vehiclecontrol computer 150 can determine the visibility distance as discussedabove and control the autonomous vehicle 105 to maintain a TTC thresholdtime. In addition, the in-vehicle control computer 150 can determine aseverity level of the visibility conditions and use the determinedseverity level as an input in determining an overall severity level asdiscussed above.

In some embodiments, the autonomous vehicle 105 is configured to operateautonomously when the visible distance can satisfy one or more thefollowing conditions: the autonomous vehicle 105 can maintain thevisibility of 8 seconds or greater, given its current speed, and theautonomous vehicle 105 cannot slow down more than ⅔ of the posted speedlimit (e.g., 50 mph in a 75 mph zone). The in-vehicle control computer150 can be configured to maintain the autonomous vehicle's 105visibility speed minimum to be independent of traction conditions.

The in-vehicle control computer 150 can further be configured to takedifferent actions based on determining the presence and severity of oneor more environmental conditions, such as rain, snow, constant wind,and/or wind gusts.

In some embodiments, the in-vehicle control computer 150 is configuredto operate the autonomous vehicle 105 in rain conditions whenaccumulations do not equal to or exceed 0.01 inch per hour. Thein-vehicle control computer 150 can further be configured to operate insnow conditions when accumulations do not equate to or exceed 0.01 inchper hour. The in-vehicle control computer 150 can also be configured tooperate under constant wind speeds that do not exceed or equate to 23MPH and within conditions of wind gusts whose speeds do not exceed orequate to 30 MPH. Of course these values are only exemplary and do notlimit the disclosure.

As used herein, a wind gust generally refers to a brief increase inspeed of the wind. According to U.S. weather observing practice, gustsare reported when the peak wind speed reaches at least 16 knots and thevariation in wind speed between the peaks and lulls is at least 9 knots.The duration of a gust is usually less than 20 seconds.

The in-vehicle control computer 150 can control the autonomous vehicle105 to take a combination of different actions in response todetermining that the autonomous vehicle 105 has entered a regionexperiencing inclement weather. One such action is a lane change. Whenthe in-vehicle control computer 150 determines that the environmentalconditions include inclement weather (e.g., any one of the levels ofseverity for each of the driving conditions is higher than normal), thein-vehicle control computer 150 may avoid changing lanes for reasonsother than ensuring the safety of the autonomous vehicle 105 or otherentities on or near the roadway. For example, the in-vehicle controlcomputer 150 may avoid controlling the autonomous vehicle 105 to changelanes for regulatory, efficiency, and/or precautionary reasons. Thein-vehicle control computer 150 can further be configured to control theautonomous vehicle 105 to perform a lane change in order to maintain thesafety of the autonomous vehicle 105 and/or another entity on or nearthe roadway.

The in-vehicle control computer 150 can also be configured to adjust alane bias of the autonomous vehicle 105 based on the determined levelsof severity for each of the driving conditions and/or the overall levelof severity for the environmental conditions. As used herein, lane biasmay refer to the lateral positioning of the autonomous vehicle 105within a lane. For example, during normal environmental conditions, thein-vehicle control computer 150 can control a lane bias of theautonomous vehicle 105 to, for example, avoid obstacles within itscurrent lane, to communicate intention to other entities on or near theroadway, etc. During inclement weather conditions (e.g., when at leastone of the driving conditions has a level of severity other than normalor at a different threshold), the in-vehicle control computer 150 canavoid performing lane bias unless lane biasing the autonomous vehicle105 would help avoid a potential collision. By avoiding lane biasing,the autonomous vehicle 105 may have increase a buffer within the lane torespond to the inclement weather (e.g., wind may push the autonomousvehicle 105 away from the center of the lane).

The in-vehicle control computer 150 can also adjust the control of theautonomous vehicle 105 when inclement weather is detected based on theshape of the roadway. For example, when the autonomous vehicle 105 isapproaching or on a curve in the roadway during inclement weather, thein-vehicle control computer 150 can adjust the speed of the autonomousvehicle 105 such that lateral acceleration of the autonomous vehicle 105is less than a predetermined value. Depending on the embodiment, thepredetermined value may be 1.5 m/s2, 2 m/s², or 2.5 m/s2, although otherthreshold values are also possible. The in-vehicle control computer 150can determine lateral acceleration of the autonomous vehicle 105 basedon the current trajectory and speed of the autonomous vehicle 105 and/orby directly measuring lateral acceleration using a sensor (e.g., an IMU)of the vehicle sensor subsystem 144.

The in-vehicle control computer 150 can also take certain actions whenapproaching a bridge during inclement weather. For example, when thein-vehicle control computer 150 determines that the autonomous vehicle105 is approaching a bridge and the outside temperature is less than athreshold value (e.g., less than 3° C., less than 4° C., less than 5° C.although other values are also possible), the in-vehicle controlcomputer 150 can adjust the speed of the autonomous vehicle 105 suchthat the autonomous vehicle 105 arrives at the bridge at a speed that isa predetermined amount slower than the speed limit. In some embodiments,the predetermined speed at which the autonomous vehicle 105 arrives atthe bridge is for example, 7%, 10%, or 15% slower that the speed limit.In other embodiments, the predetermined speed at which the autonomousvehicle 105 arrives at the bridge can be a set amount lower than thespeed limit (e.g., 5 mph or 10 mph slower than the speed limit). Thein-vehicle control computer 150 can further dynamically adjust the speedof the autonomous vehicle 105 when arriving at or traversing a bridge,for example, in response to determining that the autonomous vehicle 105is not maintaining its lateral position within the lane due to theslippery environmental conditions on the bridge.

The in-vehicle control computer 150 can, in response to detectinginclement weather, also be configured to determine whether the roadtraction of the autonomous vehicle 105 is withing a predetermined rangeof values (e.g., less than 0.8 but greater than 0.5, less than 1.0 butgreater than 0.4, etc.). In response to determining that the roadtraction is within the predetermined range, the in-vehicle controlcomputer 150 can be configured to adjust the speed of the autonomousvehicle 105 to a predetermined speed (e.g., 15 mph, 20 mph, 25 mph).

Out of Operational Design Domain (ODD)

One aspect related to the detection of environmental conditions andsafely navigating the autonomous vehicle 105 based on the detectedenvironmental conditions is the ODD of the autonomous vehicle 105. Asused herein, ODD generally refers to a set of specific conditions underwhich the autonomous vehicle 105 is designed to function. Thus, oneaspect of safely operating an autonomous vehicle 105 is to be able todetect whether the autonomous vehicle 105 is within the ODD. In order toensure that the autonomous vehicle 105 does not cause or become involvedin any collisions, the autonomous vehicle 105 can also be configured totake certain actions when the autonomous vehicle 105 detects that it isoutside of the ODD.

In some implementations, the in-vehicle control computer 150 isconfigured to cause the autonomous vehicle 105 to perform an MRCmaneuver when an out-of-ODD weather condition is encountered. Forexample, because the autonomous vehicle 105 is not designed to operatein ODD conditions, the in-vehicle control computer 150 is configured toenter a safe state to prevent harm to road users or property.

According to certain embodiments, the ODD of the autonomous vehicle 105can be classified into three categories: 1. In ODD, 2. Degraded ODD, and3. Out of ODD. The in-vehicle control computer 150 of the autonomousvehicle 105 can be configured to perform certain actions based on whichODD level the autonomous vehicle 105 is currently experiencing.Depending on the embodiment, the in-vehicle control computer 150 can beconfigured to determine which ODD level the autonomous vehicle 105 iscurrently experiencing at least in part on thresholds for ice, rain,fog, wind, and/or weather conditions. For example, in ODD may refer tono detriment to any of the above-listed conditions, degraded ODD mayrefer to a detriment to one of more of the above-listed conditions butthe autonomous vehicle 105 is still within ODD, and out of ODD may referto passing one or more of the above thresholds such that it is no longersafe to operate the autonomous vehicle 105.

One action that the autonomous vehicle 105 can take in response to beingout of ODD is an MRC maneuver. Depending on the implementation, theautonomous vehicle 105 may be configured to take one of a plurality ofdifferent MRC maneuvers (e.g., a first MRC maneuver may include pullingover to a shoulder of the roadway, a second MRC maneuver may include afull brake while staying in the current lane, and a third MRC maneuvermay include smooth braking while staying in the current lane). Afterexecuting the first MRC maneuver, the autonomous vehicle 105 may be in asafe state. As used herein, a safe state generally refers to thecondition after the autonomous vehicle 105 is stationary off thedrivable roadway and will remain stationary until further notice ordirection from the oversight system 350.

The in-vehicle control computer 150 may store a map within the memory175 that includes a map of the roadways and nearby environment (see theMap Taxonomy section of this application). The map can include safezones for which it can be safe for the autonomous vehicle 105 to performMRC maneuvers. FIGS. 6C and 6D illustrate example visualizations of thesafe zones on or near the roadway in accordance with aspects of thisdisclosure.

In some embodiments, the in-vehicle control computer 150 can beconfigured to control the autonomous vehicle 105 to perform the firstMRC maneuver (e.g., pulling over to a shoulder of the roadway) inresponse to determining that the ODD level is “Out of ODD.” Thein-vehicle control computer 150 may also consider whether the mapindicates that a safe zone is available prior to executing the first MRCmaneuver.

In some embodiments, the in-vehicle control computer 150 can beconfigured to control the autonomous vehicle 105 to perform the thirdMRC maneuver (e.g., smooth braking while staying in the current lane) inresponse to the autonomous vehicle 105 attempting to perform the firstMRC maneuver for longer than a predetermined period of time withoutsuccess. Depending on the embodiment, the predetermined period of timemay be 2 minutes, however, shorter or longer predetermined periods oftime can also be used.

The in-vehicle control computer 150 can also be configured to notify theoversight system 350 in response to determining that the ODD level is“Out of ODD.” The notification can include an indication of thelocation, time, and/or an event trigger associated with the ODD. Theevent trigger can include, for example, the environmental conditions forat least the length of time in which the autonomous vehicle 105 was inthe “Out of ODD” level.

Icy Road Detection

One environmental condition that the in-vehicle control computer 150 canbe configured to monitor and detect is whether any portion of theroadway is in an icy condition. In some embodiments, the in-vehiclecontrol computer 150 can be configured to detect traffic signsindicating the warning of an icy road at a predetermined minimumdistance in front of the autonomous vehicle 105. Depending on theimplementation, the predetermined minimum distance may be, for example,200 m, 222 m, 250 m, 275 m, 300 m, however, other predetermineddistances are also possible. In some embodiments, the autonomous vehicle105 can be configured to operate autonomously when the coefficient offriction is greater than or equal to 0.5. FIGS. 6E-6G illustrate examplevisualizations of traffic signs that may identify icy conditions inaccordance with aspects of this disclosure.

The in-vehicle control computer 150 may store a map within the memory175 that includes a map of the roadways and nearby environment (see theMap Taxonomy section of this application). The map can include thelocations of roadways and/or roadway areas which have at least apredetermined probability of becoming icy. For example, the locationsindicative of icy roadways can include any roadways for which icy roadsignage is present as well as any roadways which have been detected byan autonomous vehicle 105 in the fleet as having been icy in the past.

The in-vehicle control computer 150 can also be configured to detect anysnow and/or sleet accumulation in the roadway as well as the amount ofaccumulation, for example, using one or more of the sensors of thevehicle sensor subsystems 144. In response to determining that the snowand/or sleet accumulation is less than a threshold depth and determiningthat the coefficient of friction between the roadway and the autonomousvehicle 105 is greater than a threshold coefficient of friction, thein-vehicle control computer 150 can control the autonomous vehicle toslow down by a predetermined amount at least a predetermined distanceahead of arriving at a detected icy roadway. Examples of the thresholddepth include 0.5 inch, 0.75 inch, 1 inch, 1.25 inches, 1.5 inches; thethreshold coefficient of friction can include 0.4, 0.45, 0.5, 0.55, 0.6;a predetermined amount of slowdown can be 20%, 22%, 25%, 27% of thecurrent speed limit; and a predetermined distance can include 40 m, 45m, 50 m, and 55 m. However, other thresholds including lower or higherthresholds can also be used depending on the implementation.

In some embodiments, the in-vehicle control computer 150 can also beconfigured to change lanes depending on the detected amount of snowand/or sleet accumulation. For example, on right-hand drive roadways,the in-vehicle control computer 150 can control the autonomous vehicle105 to drive on the left side of the road (e.g., biasing to the leftside of the current lane) in response to the detected amount of snowand/or sleet accumulation being less than a threshold depth. Thethreshold depth may be 0.5 inch, 0.75 inch, 1 inch, 1.25 inches, 1.5inches.

The in-vehicle control computer 150 can be configured to detectenvironmental conditions that are indicative of black ice on theroadway. For example, the in-vehicle control computer 150 can detect thecombination of the ambient temperature being less than a thresholdtemperature and the water level near the roadway being less than 10% ofthe road crown, and in response, the in-vehicle control computer 150 cancontrol the autonomous vehicle to slow down to a predetermined speed.Depending on the embodiment, the threshold temperature may be 3° C., 4°C., 5° C., however, other threshold temperatures are also possible.

In some embodiments, the predetermined speed may be 15 mph, 20 mph, or25 mph, however, other speeds are also possible. The in-vehicle controlcomputer 150 can also determine whether the traction of the autonomousvehicle 105 is less than a predetermined threshold value, and inresponse to the traction being less than the predetermined value, thein-vehicle control computer 150 can control the autonomous vehicle 105to execute the first MRC maneuver. The predetermined threshold value maybe, for example, 0.4, 0.45, 0.5, 0.55, 0.6, while other values are alsopossible.

In response to detecting black ice, the in-vehicle control computer 150may inform the oversight system 350 and provide any associated detectedenvironmental conditions (e.g., temperature, water level, traction,etc.). The in-vehicle control computer 150 can also activate theautonomous vehicle's 105 hazard lights in response to detecting blackice.

The in-vehicle control computer 150 can also be configured to detect icereflection. For example, the in-vehicle control computer 150 candetermine whether light reflecting from snow is hindering the visibilityof one of more sensors of the vehicle sensor subsystems 144. In responseto determining that the light reflected from snow is affectingvisibility of one or more sensors, the vehicle sensor subsystems 144 cancontrol the autonomous vehicle 105 to slow down by a predeterminedamount of the current speed limit and prioritize information coming fromthe onboard localization system to continue navigation. Thepredetermined amount may include, for example, 20%, 22%, 25%, 27% of thecurrent speed limit, although other predetermined amounts are alsopossible.

Road Traction Response

As mentioned above, the in-vehicle control computer 150 can also beconfigured to monitor the road traction as one of the environmentalconditions detected by the in-vehicle control computer 150. As usedherein, road traction generally refers to the friction between a drivewheel of the autonomous vehicle 105 and the road surface. Low roadtraction generally refers to a road traction with a coefficient that isless than a threshold value, for example, 0.4, 0.5, 0.6, although othercoefficients are also possible.

The in-vehicle control computer 150 can be configured to detect adegraded traction level when the autonomous vehicle 105 is required toslow down to levels below normal speeds but still over 30 mph due toextended stopping distance or impaired maximum available decelerationrate. As used herein, a degraded traction level may generally refer to adegraded weather condition that causes a decrease to the autonomousvehicle's 105 traction due to inclement weather but is still within ODD.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect a degraded traction condition in response to one ormore of the following conditions being satisfied: the autonomous vehicle105 modifies its behavior and speed to respond to inclement weather thatcontains degraded traction levels.

In one example scenario, the in-vehicle control computer 150 can beconfigured to detect a change in traction that dictates a degradedweather response. In response, the in-vehicle control computer 150 cancause the autonomous vehicle 105 to perform one or more of the followingactions: reduce its speed to ensure a stopping distance with the maximumavailable deceleration rate always remains available, lane change withcritical intent, apply the maximum available deceleration rate or lessto decelerate, if safe to do so, lane change to the right-most lane, andmaintain a preference for the right-most lane.

The in-vehicle control computer 150 can be configured to detect acautionary traction level and slow the autonomous vehicle 105 to a speedbelow 30 mph due to an extended stopping distance or impaired maximumavailable deceleration rate. As used herein, a cautionary traction levelgenerally refers to a cautionary weather condition that causes adecrease to the autonomous vehicle's 105 traction due to inclementweather and is at risk of becoming out of ODD.

In one example scenario, the in-vehicle control computer 150 can beconfigured to detect a change in traction that dictates a cautionaryweather response. In response, the in-vehicle control computer 150 cancause the autonomous vehicle 105 to perform one or more of the followingactions: execute lane change maneuvers to the right-most lane for theduration of any cautionary weather, activate the autonomous vehicle's105 emergency lights until the cautionary weather conditions haveconcluded, reduce the autonomous vehicle's 105 speed to ensure astopping distance with maximum available deceleration rate alwaysremains available, avoid all lane changes, except for those that arerequired for safety (e.g., lane change to the right-most lane) or thoserequired to continue the autonomous vehicle's 105 mission (e.g., to takean exit), if the autonomous vehicle 105 must lane change, the autonomousvehicle 105 shall use critical lane change, apply a maximum availabledeceleration rate or less to decelerate, and lane biases to avoidcollision.

The in-vehicle control computer 150 can be configured to estimate theroad traction between the drive wheels of the autonomous vehicle 105 andthe road surface. In some embodiments, the in-vehicle control computer150 can estimate the road traction based on one or more of thedetermined environmental conditions and/or the current temperature. Thein-vehicle control computer 150 can be further configured to predict theroad traction that the autonomous vehicle 105 will experience at a giventime in the future based on the environmental conditions and/or thetemperature that the autonomous vehicle 105 is expected to experience atthe future time.

In some embodiments, the in-vehicle control computer 150 can beconfigured to determine that there is a risk of low road traction inresponse to the temperature being less than a threshold temperature dueto an increased risk of black ice being present on the roadway below thethreshold temperature. In some implementations, the thresholdtemperature may be 2° C., 3° C., 4° C., although other threshold valuesare also possible.

The in-vehicle control computer 150 can further be configured to use theroad surface type as an input in estimating the road traction. Thein-vehicle control computer 150 can determine the road surface typebased on the predetermined map and/or a determination of the roadsurface type based on the data obtained from one or more sensors of thevehicle sensor subsystems 144. Example road surface types which can bedefined in the map and/or determined based on sensor data includeasphalt, mud, sand, gravel, wet leaves.

In some embodiments, the in-vehicle control computer 150 can also beconfigured to estimate the road traction based at least in part ondetecting understeering of the autonomous vehicle 105. For example, thein-vehicle control computer 150 can determine that the autonomousvehicle 105 is experiencing a degraded road traction level based on adetected response of the autonomous vehicle 105 body to maximumacceleration while accelerating and maximum deceleration while slowingdown.

In some implementations, the in-vehicle control computer 150 can alsouse a determined wheel torque in detecting the road traction level. Asused herein, wheel torque generally refers to the sum of the torquesapplied by the autonomous vehicle's 105 powertrain and brakingactuators. The in-vehicle control computer 150 can detect the roadtraction level based on a detected response of the autonomous vehicle's105 body to the applied wheel torque. In some embodiments, thein-vehicle control computer 150 can determine the road traction levelbased on a slip rate from the tires of the autonomous vehicle 105. Thisdetermination can be supplemented with a detected temperature and/orclimate information in some implementations.

In some embodiments, when the in-vehicle control computer 150 determinesthat the road traction level is less than the threshold road tractionlevel, the in-vehicle control computer 150 can limit lateralacceleration to a threshold lateral acceleration value and limit jerk toa threshold jerk value. By limiting the lateral acceleration and jerk,the in-vehicle control computer 150 can reduce the risk of theautonomous vehicle 105 losing traction with the road surface. In someembodiments, the threshold lateral acceleration value may be 1.5 m/s2, 2m/s², 2.5 m/s2 and the threshold jerk value may be 0.75 m/s3, 1 m/s3,1.25 m/s3, 1.5 m/s3, 2 m/s3, however, other thresholds are alsopossible. The in-vehicle control computer 150 can also be configured tolimit lateral acceleration for all or substantially all lateralmaneuvers (e.g., lane change, lane bias, curbs) when the estimated ordetermined road traction level is less than the threshold value.

The in-vehicle control computer 150 can further be configured todynamically adjust a following distance from other vehicles directlyahead of the autonomous vehicle 105 (e.g., within the current lane ofthe autonomous vehicle 105) based at least in part on the estimated roadtraction level and a safe longitudinal deceleration required tocompletely stop. For example, the in-vehicle control computer 150 candetermine the safe longitudinal deceleration required to completely stopbased on the road surface type and the estimated road traction level.The in-vehicle control computer 150 can be configured to determine thefollowing distance to be greater than the safe longitudinal decelerationrequired to completely stop.

Road Water Operational Limits

Another environmental condition that the in-vehicle control computer 150can be configured to monitor is road water, which can include fog andflooding. When road water environmental conditions are present, thein-vehicle control computer 150 can be configured to adjust one or moredriving parameters of the autonomous vehicle 105 that govern the rangeof actions that can be autonomously executed by the autonomous vehicle105 such that the autonomous vehicle 105 can safely navigate thedetected road water conditions.

One aspect of detecting whether fog may be present can include thein-vehicle control computer 150 detecting fog road warning signs. Insome embodiments, the in-vehicle control computer 150 can, based on datareceived from one or more sensors of the vehicle sensor subsystems 144,detect traffic signs that provide a warning of a fog area at apredetermined minimum distance ahead of the autonomous vehicle 105. Thepredetermined minimum distance may be, for example, 200 m, 222 m, 250 m,300 m, however, other distances are also possible. FIG. 6H illustratesan example visualization of a traffic sign that may identify a fog areain accordance with aspects of this disclosure.

As discussed herein, the in-vehicle control computer 150 may store a mapwithin the memory 175 that includes a map of the roadways and nearbyenvironment (see the Map Taxonomy section of this application). The mapcan include the locations of fog areas and/or roadway areas which haveat least a predetermined probability of becoming foggy. For example, thelocations indicative of fog areas can include any roadways for which fogwarning signage is present as well as any roadways which have beendetected by an autonomous vehicle 105 in the fleet as having experiencedfog in the past.

In response to determining that the autonomous vehicle 105 is in a fogarea, the in-vehicle control computer 150 can be configured to estimatethe visibility range for one or more of the sensors of the vehiclesensor subsystems 144 based on the detected visibility of the sensors ofthe vehicle sensor subsystems 144. In addition, the in-vehicle controlcomputer 150 can be configured to adjust one or more driving parametersof the autonomous vehicle 105 in response to determining that theautonomous vehicle 105 is in a fog area and/or has reduced visibility.For example, when the estimated visibility range is less than athreshold distance, the in-vehicle control computer 150 can control theautonomous vehicle 105 to slow down by a predetermined amount at least apredetermined minimum distance before entering a fog area. In someembodiments, the threshold distance can be, for example, 45 m, 53.3 m,60 m, the predetermined amount can be, for example, 20%, 25%, 30% of thecurrent speed limit, and the predetermined minimum distance can be, forexample, 40 m, 45 m, 50 m, 55 m, although other values are alsopossible. The in-vehicle control computer 150 can also be configured toactivate the autonomous vehicle's 105 low beams prior to entering thefog area, for example, at or before the predetermined minimum distancebefore entering the fog area.

In some implementations, the in-vehicle control computer 150 can furtherbe configured to detect flooded areas, for example, by detecting thepresence of flooded road signs. The in-vehicle control computer 150 mayalso be configured to inform the oversight system 350 in response todetecting the flooded road signs such that the oversight system 350 canupdate the map and provide an indication of the flooded area to othervehicles in the fleet.

As discussed herein, the in-vehicle control computer 150 can beconfigured to compare data received from the one or more sensors of thevehicle sensor subsystems 144 to data stored in the map to provide alevel of redundancy. For example, when the data received from the one ormore sensors of the vehicle sensor subsystems 144 matched the mappeddata of the environment, the in-vehicle control computer 150 can have ahigher level of confidence that both sets of data correctly reflect thestate of the environment.

In the context of flood roadways, when there is a discrepancy betweenthe mapped flooded areas and the flooded areas determined based on dataobtained via the one or more sensors of the vehicle sensor subsystems144, the in-vehicle control computer 150 can inform the oversight system350 of the discrepancy and the location of the discrepancy and theautonomous vehicle 105.

In certain implementations, the in-vehicle control computer 150 can alsobe configured to estimate the level of the water in a flooded area, forexample, based on the road crown visibility. In response to the waterlevel being greater than a predetermined amount, the in-vehicle controlcomputer 150 can be configured to inform the oversight system 350. Thepredetermined amount can be, for example, 18%, 20%, 22%, 25% of the roadcrown, however, other amounts are also possible.

The map can also include the locations of flooded areas. For example,the locations indicative of flooded areas can include any roadways forwhich flood warning signage is present as well as any roadways whichhave been detected by an autonomous vehicle 105 in the fleet asexperiencing flooding.

The in-vehicle control computer 150 can also be configured to takeaction in response to detecting the presence of a flooded area. Forexample, the in-vehicle control computer 150 can be configured to slowthe autonomous vehicle 105 down by a predetermined amount of the currentspeed limit at least a predetermined distance before entering theflooded areas in response to determining that the level of water is lessthan a predetermined threshold amount of the road crown and thecoefficient of friction between the road and the autonomous vehicle 105is greater than a threshold coefficient of friction value. In someembodiments, the predetermined amount that the autonomous vehicle 105 isslowed can be, for example, 20%, 22%, 25%, 28%, 30% of the current speedlimit, the predetermined distance before entering the flooded areas canbe, for example, 40 m, 45 m, 50 m, 55 m, the predetermined thresholdamount of the road crown can be, for example, 18%, 20%, 22%, and thethreshold coefficient of friction value can be, for example, 0.4, 0.5,0.6. However, other threshold values can be used without departing fromaspects of this disclosure.

In some embodiments, rather than slowing down when approaching a floodedarea, the in-vehicle control computer 150 can be configured to take analternate route. For example, when the in-vehicle control computer 150determines that the level of water is greater than a predeterminedamount of the road crown, the in-vehicle control computer 150 can findan alternate route to continue the autonomous vehicle's 105 travel tothe destination. In some embodiments, the predetermined amount of theroad crown can be, for example, (e.g., 18%, 20%, 22%, 25%), althoughother values are also possible.

The in-vehicle control computer 150 can further be configured to usealternative sources of localization when one or more traffic lanemarkings are covered with water. For example, when a traffic lanemarking used in part for localization of the autonomous vehicle 105 iscovered in water, the in-vehicle control computer 150 can be configuredto use the onboard localization system to ensure that the autonomousvehicle 105 is moving in the right direction.

The in-vehicle control computer 150 can also be configured to detect andrespond to flooded and partially flooded roadways. As used herein, apartially flooded roadway generally refers to a roadway that hasdetected road water that is still within the autonomous vehicle's 105ODD. A flooded roadway generally refers to when the road water levelsare considered out of the autonomous vehicle's 105 ODD.

The in-vehicle control computer 150 can be configured to classify apartially flooded area as degraded weather condition or cautionaryweather condition in response to detecting a partially flooded roadway.The in-vehicle control computer 150 can be configured to classify aflooded roadway as a critical weather condition in response to detectinga flooded roadway.

In one example scenario, the in-vehicle control computer 150 can beconfigured to detect a partially flooded roadway and classify thepartially flooded roadway as degraded weather condition. In response,the in-vehicle control computer 150 can perform one or more of thefollowing actions: reduce the autonomous vehicle's 105 speed to ensure astopping distance with the maximum available deceleration rate alwaysremains available, lane change with critical intent and lane bias toavoid collision, apply the maximum available deceleration rate or lessto decelerate, and if safe to do so, lane change to the right-most lane.

In another example scenario, the in-vehicle control computer 150 can beconfigured to detect a partially flooded roadway and classify thepartially flooded roadway as a cautionary weather condition. Inresponse, the in-vehicle control computer 150 can perform one or more ofthe following actions: execute lane change maneuvers to the right-mostlane for the duration of the cautionary weather condition, activate theautonomous vehicle's 105 emergency lights until the cautionary weatherconditions have concluded, reduce the speed of the autonomous vehicle105 to ensure a stopping distance with the maximum availabledeceleration rate always remains available, apply the maximum availabledeceleration rate or less to decelerate, and lane bias to avoidcollision.

In yet another example scenario, the in-vehicle control computer 150 canbe configured to detect a flooded roadway and classify the floodedroadway as a critical weather condition. In response, the in-vehiclecontrol computer 150 can attempt to find an alternative route, and if analternative route to avoid flooded roadway is not found, perform one ormore of the following actions: activate the autonomous vehicle's 105emergency lights, determine a proper MRC maneuver to conduct, executethe MRC maneuver, contact the oversight system 350, and enter a safestate to protect the autonomous vehicle 105 during the extreme weatherevent and keep the hazard lights on for visibility.

In still yet another example scenario, the in-vehicle control computer150 can be configured to detect a flooded roadway ahead and classify theflooded roadway as a critical weather condition. In response, thein-vehicle control computer 150 can attempt to find an alternativeroute, and in response to finding an alternative route to avoid theflooded roadway: verify that the alternative route does not intersectwith the flooded roadway and take the alternative route to continue themission.

Headlight Usage

Another aspect related to the detection of environmental conditions isthe automated actuation of the autonomous vehicle's 105 headlights. Theautonomous vehicle 105 can be equipped with at least low-beams andhigh-beams. In some implementations, the low-beams can provide coverageof about 60.96 m (200 ft) while the high-beams can provide coverage ofabout 106.68-133.33 m (350-400 ft).

The in-vehicle control computer 150 can be configured to track the areasof the environment that are illuminated by the headlights based on adefined lighting zone. In some implementations, the in-vehicle controlcomputer 150 can define the lighting zone as an area in the roadilluminated by the autonomous vehicle's 105 high-beams. The headlightbeam can be defined to be enclosed horizontally by a horizontal spreadangle (α), where external boundaries are considered; bounded verticallyby an upward vertical spread angle (β); and the distance aheadcircumscribed to the headlight range (Rg). FIGS. 6I-6K illustrateexample visualizations of lighting zones in accordance with aspects ofthis disclosure.

The in-vehicle control computer 150 can be configured to turn on theautonomous vehicle's 105 headlights in low visibility conditions, (e.g.,fog, sun glare, rain, dust, etc.), to make the autonomous vehicle 105visible to others. The in-vehicle control computer 150 can also turns onthe low-beam headlights during inclement weather so other road usersdetect the autonomous vehicle 105, thereby improving roadway safety. Thein-vehicle control computer 150 can further engage the low-beamheadlights during inclement weather.

When in a cautionary or critical weather condition, the in-vehiclecontrol computer 150 can activate the hazard lights to alert other roadusers of cautionary driving behavior. Hazard light usage laws vary fromstate to state, for example, in Texas this use of hazard lights is ok.The in-vehicle control computer 150 can use hazard lights while incautionary or critical weather conditions.

Degraded environmental conditions such as snow, rain, fog, sleet, and/orsmoke can reduce visibility, making it difficult for the sensors to seeobjects in the environment and for the autonomous vehicle 105 to beseen. Though these conditions may depend on state or other local laws,in some embodiments, degraded visibility may be defined as visibilitythat is less than a predetermined threshold distance (e.g., 800 feet,900 feet, 1000 feet, 1100 feet) in most states in the US.

As used herein, a degraded visibility level may generally refer to thedistance calculated by the formula: [(Speed Limit (m/s))*(8 seconds)] to[(Speed Limit (m/s))*(8 seconds)*(¾)]. However, other calculations arealso possible to determine degraded visibility. Degraded visibility canalso generally refer to a degraded weather condition that causesimpairment to the autonomous vehicle's 105 visibility range due toinclement weather but is still within ODD.

In some embodiments, the in-vehicle control computer 150 can detect avisibility condition when the following condition being met: theautonomous vehicle modifies its behavior and speed to respond toinclement weather that contains degraded visibility levels.

In one example scenario, the in-vehicle control computer 150 can beconfigured to detect a change in visibility that dictates a degradedweather response. In response, the in-vehicle control computer 150 canperform one or more of the following actions: reduce the speed of theautonomous vehicle 105 to ensure a stopping distance with the maximumavailable deceleration rate always remains available, lane change withcritical intent if target back critical distance behavior can beconfirmed, if safe to do so, lane change to the right-most lane, andmaintains a preference for the right-most lane.

As used herein, a cautionary visibility level can generally refer to adistance calculated by the formula: [(Speed Limit (m/s))*(8seconds)*(¾)] to [(Speed Limit (m/s))*(8 seconds)*(⅔)]. However, othercalculations are also possible to determine degraded visibility.Cautionary visibility level can also refer to a cautionary weathercondition that causes impairment to the autonomous vehicle's 105visibility range due to inclement weather and is at risk of becoming outof ODD.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect a cautionary visibility condition when thefollowing condition are met: the autonomous vehicle 105 modifies itsbehavior and speed to respond to inclement weather that containscautionary visibility levels.

In one example scenario, the in-vehicle control computer 150 can beconfigured to detect a change in visibility that dictates a cautionaryweather response. In response, the in-vehicle control computer 150 canperform one or more of the following actions: execute lane changemaneuvers to the right-most lane for the duration of any cautionaryweather, activates the autonomous vehicle's 105 emergency lights untilthe cautionary weather conditions have concluded, reduces speed toensure a stopping distance with the maximum available deceleration ratealways remains available, avoids all lane changes, except for those thatare required for safety (e.g., lane change to the right-most lane) orthose required to continue the autonomous vehicle's 105 mission (e.g.,to take an exit), and if the autonomous vehicle 105 must lane change,the autonomous vehicle 105 should use critical lane change.

The in-vehicle control computer 150 can determine that the visibility isa degraded environmental condition when the visibility of one or more ofthe sensors in the vehicle sensor subsystems 144 is less than apredetermined threshold distance. The predetermined distance thresholdmay be, for example, 800 feet, 900 feet, 1000 feet, 1100 feet. Thein-vehicle control computer 150 can use second and third predetermineddistance threshold to identify the visibility as a cautionary orcritical environmental condition.

The in-vehicle control computer 150 can also detect the presence oftraffic signs that can indicate that the use of headlights would beappropriate. In some embodiments, the in-vehicle control computer 150can activate the headlights at least in part based on the detection ofsuch traffic signage. FIGS. 6L-60 illustrate example visualizations of atraffic signs that may identify that the use of headlights isappropriate in accordance with aspects of this disclosure.

In determining whether to activate the headlight, the in-vehicle controlcomputer 150 can perceive degraded environmental conditions (alsoreferred to as adverse or bad weather conditions) such as blizzard, highwind, snow, rain, and/or sleet.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect the presence of other entities (e.g., vehicles,pedestrians, etc.) within the lighting zone of the headlights.

In some embodiments, the in-vehicle control computer 150 can beconfigured to turn on low-beams in response to determining that theautonomous vehicle 105 is inside a tunnel.

The in-vehicle control computer 150 can further be configured to turn onthe low-beams at night or at any time defined by local (e.g., state)law. For example, when the autonomous vehicle 105 is in Pennsylvania,the in-vehicle control computer 150 can be configured to turn onlow-beams from sunset to sunrise.

The in-vehicle control computer 150 can be configured to turn onlow-beams in response to detecting degraded or more severe environmentalconditions. For example, the in-vehicle control computer 150 can beconfigured to turn on low-beams in response to detecting certain typesof inclement weather, such as: snow, rain, fog, sleet, and/or smokewhich can reduce visibility.

In some implementations, the in-vehicle control computer 150 can beconfigured to turn on low-beams in response to the autonomous vehicle's105 wipers being in continuous use for longer than a predeterminedlength of time. The predetermined length of time may be, for example, 10s, although other lengths of time are also possible. The in-vehiclecontrol computer 150 can be configured to turn on low-beams in responseto the wipers being in continuous use for longer than the predeterminedlength of time when required for compliance with local laws, such as inPennsylvania, Alabama, and California.

The in-vehicle control computer 150 can also be configured to turn onlow-beams when visibility is less than a minimum threshold value asrequired by local (e.g., state) law. For example, in Pennsylvania, theminimum threshold value may be about 304.8 m (1000 feet).

The in-vehicle control computer 150 can further be configured to turn onlow-beams in a number of other scenarios, such as in a constructionzone, when the road grade is greater than a predetermined thresholdlevel, and/or when mandated by a detected traffic sign. This use of lowbeams can increase the visibility of the autonomous vehicle 105 whendoing so can increase safety by making the autonomous vehicle 105 morevisible to other entities on the road. The predetermined threshold levelmay be, for example, a road grade of 2%, 3%, 4%, although other levelsare also possible. FIGS. 6P-6R illustrate example visualizations of atraffic signs that may identify that the use of headlights is requiredin accordance with aspects of this disclosure.

In some embodiments, the in-vehicle control computer 150 can further beconfigured to turn off high-beams in response to detecting an entity inor about to enter the lighting zone of the autonomous vehicle's 105low-beam headlights. The in-vehicle control computer 150 can also beconfigured to turn on the high-beams at night in response to detectingthe absence of other entities in the autonomous vehicle's 105 lightingzone. FIG. 6S illustrates and example visualization of one or moreentities which are within the autonomous vehicle's 105 lighting zone inaccordance with aspects of this disclosure.

Example Technique for Controlling an Autonomous Vehicle Based onEnvironmental Conditions

One objective of this disclosure includes controlling an autonomousvehicle 105 based at least in part on the current environmentalconditions in which the autonomous vehicle 105 is experiencing. FIG. 6Tillustrates an example method which can be used to control theautonomous vehicle 105 taking into account environmental conditionsdetected by at least one perception sensor. The method 600 may bedescribed herein as being performed by one or more processors, which mayinclude the in-vehicle control computer 150.

The method 600 begins at block 601. At block 602, the in-vehicle controlcomputer 150 is configured to receive perception data from at least oneperception sensor of an autonomous vehicle.

At block 604, the in-vehicle control computer 150 is configured toreceive an indication of current weather conditions from an externalweather condition source. For example, the external weather conditionsource can include the United States Weather Service.

At block 606, the in-vehicle control computer 150 is configured todetermine a current environmental condition severity level from aplurality of severity levels based on the perception data and theindication of current weather conditions.

At block 608, the in-vehicle control computer 150 is configured tomodify one or more driving parameters that govern a range of actionsthat can be autonomously executed by the autonomous vehicle. The drivingparameters may include instructions for controlling movement of theautonomous vehicle 105 based on one or more of: environmentalconditions, other vehicles and/or object on the roadways, thelocalization of the autonomous vehicle 105, the current route of theautonomous vehicle 105, etc.

At block 610, the in-vehicle control computer 150 is configured tonavigate the autonomous vehicle based on the modified one or moredriving parameters. The method 600 ends at block 610.

General Driving Behavior

The autonomous vehicle 105 can be configured to perform a number ofdifferent tasks while navigating under normal or general drivingconditions. For example, the autonomous vehicle 105 can be configuredto, under normal or optimal conditions, operate according to a defaultdecision framework that includes certain behavior associated with theautonomous vehicle's 105 trailer load, recognizing and responding totraffic signs, and maintaining proper positioning within the autonomousvehicle's 105 lane.

In certain embodiments, the autonomous vehicle 105 can include anin-vehicle control computer 150 configured to estimate a grade of theroadway based on perception data indicative of one or more parameters ofthe roadway received from one or more perception sensors of anautonomous vehicle, provide a first control input to the autonomousvehicle based on the grade of the roadway, determine a response of theautonomous vehicle to the first control input based on perception dataindicative of movement of the autonomous vehicle received from the oneor more perception sensors, estimate a trailer load of the trailer basedon the response of the autonomous vehicle to the first control input,and provide a second control input to the autonomous vehicle based onthe grade of the roadway and the trailer load. Accordingly, thein-vehicle control computer 150 can adjust controls provided to theautonomous vehicle 105 in order to respond to a determined road gradeand trailer load without needing to have the road grade and trailer loadmanually input into the autonomous vehicle 105.

Trailer Load Behavior

One important aspect involved in safely navigating an autonomous vehicle105 is the detection of and response to various conditions that canaffect the autonomous vehicle's 105 behavior when operating with aloaded trailer.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect or estimate the road grade in front of theautonomous vehicle 105. The in-vehicle control computer 150 can also beconfigured to dynamically adjust throttle (e.g., acceleration) and brake(e.g., deceleration) control based at least in part on the detected roadgrade.

The in-vehicle control computer 150 can also be configured to receivethe information about the type of load and cargo in the yard. Forexample, the in-vehicle control computer 150 can receive the load/cargoconfiguration of one or more of the following: the trailer type, thecargo type, and/or the type of load. Example trailer types include: dryvan, flatbed, and refrigerated. Further example trailer types include:53′ dry van trailer with standard dimensions, 53′ refrigerated trailerswith standard dimensions, 48′ dry van trailer with standard dimensions,and 48′ refrigerated trailers with standard dimensions. Example cargotypes include: large equipment, consumer goods, raw materials, etc.Example load types include: fully loaded, partially loaded, empty, etc.

In some embodiments, the in-vehicle control computer 150 can beconfigured to estimate trailer load from the autonomous vehicle's 105acceleration response based on throttle and/or braking inputs. Thein-vehicle control computer 150 can also consider the relationshipbetween the requested wheel torque and the autonomous vehicle's 105acceleration to estimate the mass of the trailer. The in-vehicle controlcomputer 150 can further be configured to estimate the force that can beused to move the trailer to adjust the throttle dynamically and brakecontrol to compensate for trailer load based on the wheel torque.

The in-vehicle control computer 150 can also be configured to detect theroad curvature in front of the autonomous vehicle 105 with a distancegreater than a stopping distance to limit lateral acceleration andmaintain stability in curves. That is, the in-vehicle control computer150 can be configured to adjust lateral acceleration and maintainstability in curves using the detected road curvature. Depending on theembodiment, the in-vehicle control computer 150 can cause the autonomousvehicle 105 to decelerate at rates when stopping the autonomous vehicle105. The in-vehicle control computer 150 can also be configured to limitlateral acceleration in the curves based on a distance that can reducethe vehicle speed to limit lateral acceleration in the curves takinginto account the braking capabilities of the autonomous vehicle 105. Thein-vehicle control computer 150 can also be configured to reduce theautonomous vehicle's 105 speed to limit lateral acceleration based onthe roadway's detected maximum curvature.

In some embodiments, the in-vehicle control computer 150 can beconfigured to dynamically adjust the throttle control to compensate forthe determined trailer load and the road grade in order to provide alongitudinal control robustness for the throttle control. The in-vehiclecontrol computer 150 can also be configured to dynamically adjust thebrake control to compensate for the determined trailer load and the roadgrade in order to provide a longitudinal control robustness for thebrake control. The in-vehicle control computer 150 can further beconfigured to dynamically adjust the steering control to compensate forside wind effects and a super elevation rate due to trailer load inorder to provide a lateral control robustness of the steering control.As used herein, the super elevation rate may generally refer to a ratioof the roadway slope to width. The in-vehicle control computer 150 canbe configured to control the lateral position of the autonomous vehicle105 to compensate for lateral resistive forces. FIG. 7A illustrates anexample visualization of the forces (e.g., lateral resistive forces)which may be relevant to an autonomous vehicle 105 driving on a roadwaywith an incline. For example, the autonomous vehicle 105 may experiencea lateral resistive force 720 and a force due to gravity 722.

In some embodiments, the in-vehicle control computer 150 can beconfigured to limit lateral acceleration to a predetermined accelerationand a predetermined jerk value to maintain the stability of the truck(e.g., not flip over) when turning or driving on curved roads takinginto account the super elevation rate. The predetermined accelerationcan include, for example, 1 m/s2, 2 m/s², although other values are alsopossible without departing from aspects of this disclosure. Thein-vehicle control computer 150 can also be configured to limit lateraldynamics for some or all lateral maneuvers (lane change, lane bias,curbs) of the autonomous vehicle 105 depending on trailer inertia andstability criteria. The in-vehicle control computer 150 can beconfigured to reduce speed of the autonomous vehicle 105 to maintain alateral acceleration. The in-vehicle control computer 150 can also beconfigured to limit the steering wheel angle velocity to limit thelateral jerk.

In some embodiments, the in-vehicle control computer 150 can beconfigured to decelerate the autonomous vehicle 105 with a maximumdeceleration of up to 5 m/s² and a maximum jerk value up to 2 m/s3,depending on the determine type of load. However, the maximumdeceleration and maximum jerk can vary depending on the particularembodiment.

The in-vehicle control computer 150 can also be configured to acceleratethe autonomous vehicle 105 with a maximum acceleration depending on thedetermined load and a maximum jerk depending on the determined type ofload. The maximum acceleration can range from 1 m/s² to 3 m/s2 and themaximum jerk can range from 1 m/s3 to 2 m/s3, however, other ranges arealso possible.

In some embodiments, the in-vehicle control computer 150 can beconfigured to increase and anticipate a following distance to a vehicleahead of the autonomous vehicle 105 to account for the allowedlongitudinal acceleration and jerk due to the determined trailer load,road grade, and traction. In adjusting the following distance, thein-vehicle control computer 150 can also be configured to considerbraking capabilities such as the maximal longitudinal accelerationachievable depending on one or more of the following: the type oftrailer, load, cargo, road grade, and/or traction.

Traffic Signs

Another important aspect involved in safely navigating an autonomousvehicle 105 is the detection of traffic signs as well as properly takingaction based on the detection of traffic signs. Thus, the in-vehiclecontrol computer 150 can be configured to detect the presence and typeof traffic signs based on perception data generated by one or moresensors of the vehicle sensor subsystems 144. It is desirable for theautonomous vehicle 105 to also detect the information communicated bythe traffic signs so that the autonomous vehicle 105 can follow anydirections indicated by the traffic signs. The in-vehicle controlcomputer 150 can also be configured to compare the information detectedon a traffic sign with corresponding traffic sign information includedin the map of the roadways and nearby environment (see the Map Taxonomysection of this application). By comparing the detected traffic signinformation with the traffic sign information from the map, thein-vehicle control computer 150 can provide a level of redundancy andhigher confidence in the detected traffic sign information.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect all traffic signs at a predetermined minimumdistance away from the autonomous vehicle 105 measured from thefrontmost point of the autonomous vehicle 105, unless the traffic signis obstructed from view. The predetermined minimum distance away fromthe autonomous vehicle 105 can include, for example, 200 m, 300 m, 400m, although other distances are also possible without departing fromaspects of this disclosure.

The in-vehicle control computer 150 can be configured to update the mapwith the identifications and locations of all traffic signs. Forexample, if the in-vehicle control computer 150 determines that adetected traffic sign is missing from the map, the in-vehicle controlcomputer 150 can be configured to update the map to include the newlydetected traffic sign.

The in-vehicle control computer 150 can be configured to take specificactions depending on the type of traffic sign that has been detected.For example, the in-vehicle control computer 150 can be configured totake actions in response to detecting each of the following traffic signtypes: stop signs, yield signs, no turn signs, no right turn signs,right turn only signs, no left turn signs, no trucks signs, pedestriancrossing signs, truck route signs, weight limit signs, road closuresigns, mandatory freeway exit signs, environment precaution signs,reduced speed limit ahead signs, merging signs, non-vehicular signs, andadvanced turn signs.

The in-vehicle control computer 150 can be configured to control theautonomous vehicle 105 to take particular actions in response todetecting a stop sign.

In response to detecting a yield sign, the in-vehicle control computer150 can be configured to slow the autonomous vehicle 105 down, to acomplete stop, if necessary, in order to let other vehicles with theright-of-way pass before proceeding. FIGS. 7B-7C illustrate examplevisualizations of yield signs.

In response to detecting a no turn sign, the in-vehicle control computer150 can be configured to only initiate turns on roads that do not have ano-turns sign. FIGS. 7D-7E illustrate example visualizations of no-turnsigns.

In response to detecting a no-right-turn sign, the in-vehicle controlcomputer 150 can be configured to only initiate right turns when theautonomous vehicle 105 is not on a designated no-right-turn road. FIG.7F illustrates an example visualization of a no-right-turn sign.

In response to detecting a right-turn-only sign, the in-vehicle controlcomputer 150 can be configured to control the autonomous vehicle 105 toperform a right turn if it is in a designated right-turn-only lane. If aright turn is not intended and the autonomous vehicle 105 has not passedthe solid white line represented by a right-turn-only lane, then thein-vehicle control computer 150 can be configured to control theautonomous vehicle 105 to initiate a lane change. In some cases,right-turn-only lanes can be initiated by a dotted white line becoming asolid white line and contains one of the signs in FIGS. 7G-7I. FIGS.7G-7I illustrate example visualizations of signs which includeright-turn-only lanes.

In response to detecting a no-left-turn sign, the in-vehicle controlcomputer 150 can be configured to control the autonomous vehicle 105 toonly perform left hand turns when the autonomous vehicle 105 is not at adesignated no-left-turn intersection. FIGS. 7J-K illustrate examplevisualizations of no-left-turn signs.

In response to detecting a left turn only sign, the in-vehicle controlcomputer 150 can be configured to control the autonomous vehicle 105 toperform a left turn if the autonomous vehicle 105 is in a designatedleft-turn-only lane. If a left turn is not intended and the autonomousvehicle 105 has not passed the solid white line represented by aleft-turn-only lane, then the in-vehicle control computer 150 can beconfigured to control the autonomous vehicle 105 to initiate a lanechange. Left-turn-only lanes can be initiated by a dotted white linebecoming a solid white line and contains one of the signs indicated inFIGS. 7L-7M. FIGS. 7L-7M illustrate example visualizations of signswhich include left-turn-only lanes.

In response to detecting a no-trucks sign, the in-vehicle controlcomputer 150 can be configured to control the autonomous vehicle 105 toexit the road and reroute. FIG. 7N illustrates an example visualizationof a no-trucks sign.

The in-vehicle control computer 150 can be configured to control theautonomous vehicle 105 to take particular actions in response todetecting a pedestrian crossing sign. FIGS. 70 -V illustrate examplevisualizations of pedestrian-crossing signs.

In response to detecting a truck-route sign, the in-vehicle controlcomputer 150 can be configured to control the autonomous vehicle 105 toabide by a truck-route lane instruction. FIG. 7W illustrates an examplevisualization of a truck-route sign.

In response to detecting a weight-limit sign, the in-vehicle controlcomputer 150 can be configured to, in response to determining that theautonomous vehicle 105 does not meet the weight requirement at adesignated weight station, control the autonomous vehicle 106 to exitthe road and reroute. FIGS. 7X-7AC illustrate example visualizations ofweight-limit signs.

In response to detecting a road-closure sign, the in-vehicle controlcomputer 150 can be configured to control the autonomous vehicle 105 toexit the road and reroute. FIGS. 7AD-7AF illustrate examplevisualizations of road-closure signs.

In response to detecting a mandatory-freeway-exit sign, the in-vehiclecontrol computer 150 can be configured to control the autonomous vehicle105 to exit the road and reroute. FIGS. 7AG-7AK illustrate examplevisualizations of mandatory-freeway-exit signs.

In response to detecting an environment-precaution sign, the in-vehiclecontrol computer 150 can be configured to control the autonomous vehicle105 to decelerate, such that the autonomous vehicle's speed ranges, forexample, from 25-55 MPH, until the environmental conditions are cleared.FIGS. 7AL-7AZ illustrate example visualizations ofenvironment-precaution signs.

In response to detecting a reduced-speed-limit ahead sign, thein-vehicle control computer 150 can be configured to control theautonomous vehicle 105 to proceed to decelerate safely between 1-2 m/s2until the new speed limit is reached. FIGS. 7BA-7BB illustrate examplevisualizations of reduced-speed-limit ahead signs.

In response to detecting a merging sign, the in-vehicle control computer150 can be configured to control the autonomous vehicle 105 to acceptmerge-ins from other vehicles. FIGS. 7BC-7BJ illustrate examplevisualizations of merging signs.

In response to detecting a non-vehicular warning sign, the in-vehiclecontrol computer 150 can be configured to control the autonomous vehicle105 to decelerate, to a speed ranging from 25 MPH to 55 MPH, until thearea is cleared. FIGS. 7BK-7BT illustrate example visualizations ofnon-vehicular warning signs.

In response to detecting an advanced-turn sign, the in-vehicle controlcomputer 150 can be configured to control the autonomous vehicle 105 todecelerate until the advanced turn is completed. FIGS. 7BU-BX illustrateexample visualizations of advanced-turn signs.

Lane Keeping

Another important aspect involved in safely navigating an autonomousvehicle 105 is the maintaining the autonomous vehicle 105 within itslane when not performing any lane changes. For example, the in-vehiclecontrol computer 150 can be configured to ensure that theoutermost-points of the autonomous vehicle 105 (e.g., including atractor and trailer when the autonomous vehicle 105 is embodied as atruck), including any portion of the trailer, remain within the insideedges of the lane boundaries at all times, unless changing lanes, doinga critical safety bias, evading, turning at an intersection, and/or theautonomous vehicle 105 is unable to remain within the lane boundariesdue to a combination of a width of the lane and a road curvature.

While keeping the autonomous vehicle 105 within its lane, the in-vehiclecontrol computer 150 can be configured to target a lateral position inthe lane such that the widest points of the autonomous vehicle 105 areequidistant from the lane boundaries when driving straight, turning, orchanging lanes, unless for an evasive maneuver, bias, or to minimizeoff-tracking. Advantageously, targeting an equidistant position can helpincrease or maximize the buffer between the autonomous vehicle 105 andthe lane boundaries.

The in-vehicle control computer 150 can also be configured to monitorany deviations by the autonomous vehicle 105 from the targeted lateralposition. In response to determining that the autonomous vehicle 105 hasdeviated from the targeted lateral position by more than a predetermineddeviation distance, the in-vehicle control computer 150 can beconfigured to return to the targeted lateral position within apredetermined deviation time. The predetermined deviation distance caninclude, for example, 10 cm, 15 cm, 20 cm and the predetermined nominaldeviation time can include, for example, 2 seconds, 3 seconds, 4seconds, although other values are also possible.

While the in-vehicle control computer 150 is lane keeping the autonomousvehicle 105, the in-vehicle control computer 150 can be configured tomaintain the magnitude of the autonomous vehicle's 105 lateralacceleration to be less than a predetermined nominal value. Thepredetermined nominal value can include, for example, 0.5 m/s2, 0.78m/s2, 1.0 m/s2, although other values are also possible withoutdeparting from this disclosure.

While the in-vehicle control computer 150 is lane keeping the autonomousvehicle 105, the in-vehicle control computer 150 can be configured tomaintain the magnitude of the autonomous vehicle's 105 lateral jerk tobe less than a predetermined nominal value. The predetermined nominalvalue can include, for example, 1.5 m/s3, 1.67 m/s3, 1.8 m/s3, althoughother values are also possible.

The in-vehicle control computer 150 can be configured to determine thelateral distance of the autonomous vehicle 105 from marked or estimatedlane boundaries with an accuracy of at least a predetermined nominaldistance. The predetermined nominal distance can include, for example,10 cm, 15 cm, 20 cm, 25 cm, although other values are also possible.

If lane markings are not present for either lane boundary, thein-vehicle control computer 150 can estimate the position of the laneboundary markings using the information from the last known laneboundary markings, any visible upcoming lane boundary markings, and/orthe paths of nearby vehicles, if present.

Example Technique for Controlling an Autonomous Vehicle Based on anEstimated Trailer Load

One objective of this disclosure includes controlling an autonomousvehicle 105 based on estimating a trailer load of the autonomous vehicle105. FIG. 7BY illustrates an example method which can be used to controlthe autonomous vehicle 105 based on the estimation of the trailer load.The method 700 may be described herein as being performed by one or moreprocessors, which may include the in-vehicle control computer 150.

The method 700 begins at block 701. At block 702, the in-vehicle controlcomputer 150 is configured to estimate a grade of the roadway based onperception data indicative of one or more parameters of the roadwayreceived from one or more perception sensors of an autonomous vehicle.

At block 704, the in-vehicle control computer 150 is configured toprovide a first control input to the autonomous vehicle based on thegrade of the roadway. The control input can include steering, throttle,and/or brake controls for controlling the autonomous vehicle 105.

At block 706, the in-vehicle control computer 150 is configured todetermine a response of the autonomous vehicle to the first controlinput based on perception data indicative of the movement of theautonomous vehicle received from the one or more perception sensors. Forexample, the perception data can include a wheel torque or accelerationof the autonomous vehicle 105.

At block 708, the in-vehicle control computer 150 is configured toestimate a trailer load of the trailer based on the response of theautonomous vehicle to the first control input. At block 710, thein-vehicle control computer 150 is configured to provide a secondcontrol input to the autonomous vehicle based on the grade of theroadway and the trailer load. The method 700 ends at block 712.

Object Detection and Response

As part of the navigation and control of the autonomous vehicle 105, theautonomous vehicle 105 can be configured to detect and respond tovarious objects present on or near the roadway. Example objects whichcan be relevant to the navigation of the autonomous vehicle 105 includeschool buses and animals. Depending on the status of a detected object,the autonomous vehicle 105 can be configured to take different actionsin order to ensure the safety of all road users.

School Buses

One important aspect involved in safely navigating an autonomous vehicle105 is the detection of and response to school buses. As used herein, aschool bus may generally refer to a vehicle used for the transportationof any school pupil at or below the 12th-grade level to or from a schoolor school-related activity. In some embodiments, the in-vehicle controlcomputer 150 can be configured to detect that a vehicle is a school busin response to determining that the vehicle's color is yellow (nationalschool bus glossy yellow), the words “school bus” appear on the frontand end of the vehicle, and/or flashing amber lights are located on thefront and rear of the vehicle.

There may be at least four main identifications that are standardnationally for school buses. The first identification is that schoolbuses may have the text “School Bus” printed in letters in black colornot less than eight inches high, located between the warning signallamps as high as possible without impairing visibility of the letteringfrom both front and rear, and/or have no other lettering on the front orrear of the vehicle. The second identification is that school buses mayhave 8 warning signal lamps located on each side of both the front andrear of the school bus, located as high as possible. The thirdidentification is that school buses may be painted in National SchoolBus Glossy Yellow, in accordance with the colorimetric specification ofNational Institute of Standards and Technology (NIST) Federal StandardNo. 595a, Color 13432, except that the hood may be either that color orlusterless black, matching NIST Federal Standard No. 595a, Color 37038.The fourth identification is that school buses may have bumpers ofglossy black, matching NIST Federal Standard No. 595a, Color 17038,unless, for increased visibility, they are covered with a reflectivematerial.

There are several types of school buses that are different by size andcapacity (i.e., number of passengers). School buses can vary from a bigvan to a big bus that can fit up to 90 passengers. All school buses mayfollow the requirements mentioned above.

The in-vehicle control computer 150 can be configured to detect a schoolbus and its associated lane from a predetermined minimum distance. Thepredetermined minimum distance can include, for example, 200 m away, 250m away, 300 m away, although other values are also possible withoutdeparting from this disclosure.

The in-vehicle control computer 150 can also be configured to detect thetext “School Bus” printed in black letters on both the rear and front ofthe school bus from a predetermined minimum distance. The predeterminedminimum distance can include, for example, 100 m away, 150 m away, 200 maway, although other distances are also possible. The in-vehicle controlcomputer 150 can be configured to detect the text “School Bus” printedin black letters on the rear side of the school bus when following orapproaching the school bus in the same direction. The in-vehicle controlcomputer 150 can further be configured to detect the text “School Bus”printed in black letters on the front side of the school bus whenapproaching the school bus from the opposite direction.

In general, school buses may have the text “School Bus” printed in blackletters not less than eight inches high, located between the warningsignal lamps as high as possible without impairing visibility of thelettering from both front and rear, and have no other lettering on thefront or rear of the vehicle.

The in-vehicle control computer 150 can be configured to detect flashingamber lights located on both the rear and front of the school bus from apredetermined minimum distance. The predetermined distance can include,for example, 200 m, 250 m, 300 m, although other distances are alsopossible without departing from this disclosure. The in-vehicle controlcomputer 150 can also be configured to detect the flashing amber lightson the rear side of the school bus when following or approaching it inthe same direction. The in-vehicle control computer 150 can further beconfigured to detect the flashing amber lights on the front side of theschool bus when approaching it from the opposite direction. Thein-vehicle control computer 150 can be configured to detect the flashinglights when the flashing lights are either red or yellow.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect the extended stop sign arm of a school bus from apredetermined minimum distance. The predetermined distance can include,for example, 200 m, 250 m, 300 m, although other values are alsopossible. The in-vehicle control computer 150 can be configured todetect the extended stop sign arm in response to determining that thestop sign arm is located on the driver side of the school bus. Thein-vehicle control computer 150 can be also configured to detect theextended stop sign arm of the school bus in response to detecting thatthe extended stop sign arm has two red flashing amber lights on top andbottom of the stop sign.

In order to ensure safety for all road users, the in-vehicle controlcomputer 150 can be configured to safely interact with any and alldetected school buses. For example, the in-vehicle control computer 150can be configured to maintain a predetermined minimum safe distance whenfollowing a school bus as the target vehicle. The predetermined minimumsafe distance can include, for example, 75 m, 100 m, 125 m, althoughother values are also possible.

In some embodiments, the in-vehicle control computer 150 can beconfigured to pass a detected school bus only when the in-vehiclecontrol computer 150 determines that it is safe to do so. When passing aschool bus, the in-vehicle control computer 150 can be configured tochange lanes from the lane that the school bus is located in from apredetermined minimum distance away from the school bus. Thepredetermined minimum distance can include, for example, 75 m, 100 m,125 m, although other distances are also possible. The in-vehiclecontrol computer 150 can be configured to cancel an attempt to pass aschool bus in response to detecting that the school bus's flashing amberlights are on.

In response to detecting a school bus with flashing lights, thein-vehicle control computer 150 can be configured to reduce the speed ofthe autonomous vehicle 105 to maintain a predetermined minimum safedistance when travelling in the same direction with the school bus,while staying in the same lane. The predetermined minimum safe distancecan include, for example, 75 m, 100 m, 125 m. The in-vehicle controlcomputer 150 can also be configured to reduce the speed of theautonomous vehicle 105 and prepare for a full stop in response todetecting the yellow flashing amber light of the school bus is on.

The in-vehicle control computer 150 can be configured to have apredetermined minimum safe distance from the school bus when the schoolbus is completely stopped. The predetermined minimum safe distance caninclude 20 m, 25 m, 30 m, although other values are also possible. Thein-vehicle control computer 150 can be configured to pass a school buswhen detecting that the school bus has its yellow flashing amber lighton if the autonomous vehicle 105 is approaching the school bus from theopposite direction, the roadway has 4 or more lanes, and there is amedian or physical barrier between the lanes.

Yellow flashing lights can be used to indicate that the school bus ispreparing to stop to load or unload children. Motorists may slow downand prepare to stop their vehicles. A school bus turns its yellowflashing amber light on at least 5 seconds or 100 m before a full stop.This depends on the initial speed of the school bus and posted speedlimit of the road. Laws and regulations vary by different states.

In some embodiments, in response to detecting a school bus with itslights on, the in-vehicle control computer 150 can be configured toreduce the speed of and stop the autonomous vehicle 105 completely aminimum number of meters away from the school bus, while staying in thesame lane. The minimum number of meters can include, for example, 15 m,20 m, 25 m, although other distances are also possible. The in-vehiclecontrol computer 150 can be configured to pass a school bus with redflashing amber light on if the autonomous vehicle 105 is approaching theschool bus from the opposite direction, the roadway has 4 or more lanes,and there is a median or physical barrier between the lanes.

Red flashing lights and extended stop arms may indicate the bus hasstopped and children are getting on or off. Motorists must stop theircars and wait until the red lights stop flashing, the extended stop-armis withdrawn, and the bus begins moving before they can start drivingagain.

In some embodiments, the in-vehicle control computer 150 can beconfigured to stay at a full stop until detecting that the school bus'sred flashing amber lights are turned off, the extended stop arm iswithdrawn, and the bus begins moving.

Animal Detection

Another important aspect involved in safely navigating an autonomousvehicle 105 is the detection of and response to animals. The in-vehiclecontrol computer 150 can be configured to detect animals withoutreliance on the map and including all metadata. In some embodiments, thein-vehicle control computer 150 can be configured to detect animals onthe roadway that are larger than an adult coyote (about 7 inches whenlying flat). The in-vehicle control computer 150 can be configured todifferentiate between a live animal and roadkill.

In response to detecting a large animal on the road moving in anydirection or on the roadside and moving towards the road, the in-vehiclecontrol computer 150 can be configured to adjust the maneuvering of theautonomous vehicle 105 to maintain distances longer than the minimum gapuntil the autonomous vehicle 105 passes the animal. As used herein,large animal may generally refer to an animal that is large enough tocause damage to the autonomous vehicle 105 if impacted at highwayspeeds, like bears, boars and deer.

In response to detecting a small animal on highways, the in-vehiclecontrol computer 150 can be configured to not respond. As used herein, asmall animal may generally refer to an animal that is too small to causedamage to the autonomous vehicle 105 if impacted at highway speeds, likedogs, cats, and birds, etc. In response to detecting a small animal on asurface road, the in-vehicle control computer 150 can be configured toattempt to prevent the autonomous vehicle 105 from impacting with thesmall animal. If the in-vehicle control computer 150 cannot avoidimpact, the in-vehicle control computer 150 can be configured to notrespond to the small animal. When unable to react to a small animal, thein-vehicle control computer 150 can be configured to react as describedto the Crash Mitigation Strategy section of this disclosure.

In response to detecting a live animal larger than an adult coyote inthe autonomous vehicle's travel lane, the in-vehicle control computer150 can be configured to reduce the speed of the autonomous vehicle 105to avoid a collision and blow the city horn to encourage the animal toleave.

Map Taxonomy

In certain embodiments, a digital map with high precision positioningdata for roadways and surroundings can be pre-developed and stored inthe memory 175 of the in-vehicle control computer or vehicle computerunit (VCU) 150 of the autonomous vehicle 105 shown in FIG. 1 . When theautonomous vehicle 105 traverses from one location to another, theglobal positioning unit in the vehicle sensor subsystems 144 receivesreal-time location or GPS data to guide the autonomous vehicle 105 todrive on roadways according to the digital map stored in the memory 175.Visual perception sensors of the vehicle sensor subsystems 144,including one or more camera, radar and LIDAR devices, can continuouslygenerate real time information about road conditions and surroundings(e.g., process 205 in FIG. 2 ). In certain embodiments, these real timeroad conditions and surroundings data, together with the real time GPSdata are transmitted to the in-vehicle control computer 150, as shown inFIG. 2 as process 210. Then, in certain embodiments, the in-vehiclecontrol computer 150 fuses the real time road data and GPS data todescribe the real time road condition and location.

Therefore, the map stored in the memory 175 on the in-vehicle controlcomputer 150 has different layers including at least a more permanentlayer and a dynamic layer. The permanent map can be updated on aperiodic basis as newer versions are transmitted from the oversightsystem 350, which collects updated map data from all autonomous vehicles105 connected over the wireless network 370 and fuses the updated mapdata into the permanent map. For example, if a newly appeared item ispresent on a roadway for a long enough period, e.g., a traffic sign, theoversight system 350 will decide to add the item into the permanent map.The layer of the dynamic, more temporary map on the in-vehicle controlcomputer 150 is based on the permanent map, and gets improved by what isdetected on the road by the vehicle sensor subsystems 144. As such, thisdynamic map may contain temporary map elements such as constructionzones or lane closures, for example. The operation of the autonomousvehicle 105 takes priority of the dynamic map over the permanent mapwhen driving on the roadways.

Therefore, there are different data streams for mapped data. On onehand, the in-vehicle control computer 150 is continuously fed with realtime road conditions data from the vehicle sensor subsystems 144. Thein-vehicle control computer 150 then compares the real time perceptiondata with the stored map data from the dynamic map. On the other hand,there are different layers of map data stored on the in-vehicle controlcomputer 150. When the in-vehicle control computer 150 compares the realtime road conditions data with the mapped data stored in the memory 175and sees a discrepancy, the real time road data precepted by the vehiclesensor subsystems 144 takes priority. In certain embodiments, thein-vehicle control computer 150 may decide to update to map data(dynamic map) and to transmit the updated map data to the control centeror oversight system 350 via the wireless network 370, as shown in FIG. 3. The updated map data can then be shared periodically with otherautonomous vehicles 105 that are in communication with the oversightsystem 350. The following sections describe the handling of thein-vehicle control computer 150 of mapped roadway information stored inthe memory 175, the detection of real time information using the vehiclesensor subsystems 144, and plans developed to manage special situations.In the context of this disclosure, the digital map is also referred toas the map, and map data is also referred to as map information, data ofthe map, mapped data, or mapped information.

Safety Area Requirements

In certain embodiments, the digital map stored on the computer memory175 of the autonomous vehicle 105 was pre-developed and implementedbefore the operation of the autonomous vehicle 105. In certainembodiments, the map can be refined and updated with newly detected datawhen the autonomous vehicle 105 traverses on the road. It may bedesirable, in certain embodiments, for the map to satisfy certainrequirements, e.g., for the minimal risk condition (MRC) requirementsfor truck maneuver.

The digital map can include safety areas along highways for theautonomous vehicle 105 to stop when necessary. For example, for eachoff-ramp exit on a highway, there may be at least two safety areasmarked on the map within 5 miles from the exit.

In certain embodiments, the safety areas may satisfy the followingrequirements. First, each safety area can be at least 96 meters (315feet) long and 3.4 meters (11.2 feet) wide if it is defined by softboundaries, or 3.6 meters (11.8 feet) wide if it is defined by hardboundaries. This can be advantageous by enabling a vehicle entry speedof 30 miles per hour (MPH), or 48 kilometers per hour (KMPH). Secondly,the safety area can be clear of permanent road hazards with a sizebigger than 15 cm (6 inch) in height and wider than the lateralwheelbase of the vehicle. In some embodiments, each carriageway in thesafety area may be paved. Further, it may be desirable that the safetyarea is not located on an overpass bridge. In certain embodiments, thesafety area may include areas that do not have a no entry traffic signand exclude any gore areas. In certain embodiments, the map can beconfigured to cover surrounding areas, e.g., 280 meters (919 feet),beyond the safety area in a longitudinal direction.

Road Taxonomy

In certain embodiments, roadways are defined and classified according totheir types in the map so that different plans can be developed for theautonomous vehicle 105 and different actions can be taken based onpre-developed algorithms. The roadway definitions can conform to thelaws and regulations and even customary local rules. The roadway, orroad, definitions and/or classifications disclosed herein representexamples, not all, of the instants stored in the map. In the context ofthis disclosure, definition and classification can be synonyms to eachother.

Roadways within the digital map can be defined as mapped roadways.

Drivable roadways within the map may be defined as the areas that avehicle can occupy, including restricted areas. Example drivableroadways includes roads, lanes of carriageway, and other areas that theautonomous vehicle 105 can drive on.

A limited access highway can be defined as a road type that is designedfor high-speed traffic with limited access traffic flow, e.g., a dualcarriageway divided by a median strip shown in FIG. 8A which is a photoshowing an example of a limited access highway. On the other hand, anon-limited access highway is classified as a road type that is designedfor high-speed traffic but is highly accessible.

A local roadway can be defined as a street that accepts traffic fromcollector roads and distributes traffic through subdivisions,neighborhoods, and business areas in a city, town, or village to homes,apartments, business sites, and industrial sites. An arterial roadgenerally refers to a high-capacity urban road which delivers trafficbetween collector roads and highways. A collector road may generallyrefer to a low-to-moderate capacity road which serves to move trafficfrom local streets to arterial roads. Unlike arterials, in certainembodiments, collector roads can be designed to provide access toresidential properties.

A single-lane road may generally refer to a road that permits two-waytravel, but is not wide enough in most places to allow vehicles to passone another. An example of the single-lane road is shown in FIG. 8Bwhere the road is divided into two single-lane roads, one for eachdriving direction. In comparison, a multi-lane road can be a road thathas two or more lanes of traffic traveling in the same direction with nophysical barriers separating the lanes. An example of the multi-laneroad is illustrated in FIG. 8C. For example, the area defined by a box802 in FIG. 8C covers approximately three lanes driving in the samedirection.

A divided roadway can be classified as a roadway where the lanes ofopposing traffic directions are divided by a physical barrier, e.g., amedian. An example divided roadway is shown in FIG. 8D. A median is anarea in the middle of a divided roadway. The median includes an areabetween the opposing direction lanes defined by hard or soft boundaries,as shown in FIG. 8D as the area covered by a rectangular box 804.

An undivided roadway is defined as a roadway where the lanes of theopposing traffic directions are separated only by lane marks, but not byphysical barrier. FIG. 8B illustrates an example of the undividedroadway.

A one-way road is a road where traffic moves only in a single direction,as shown in FIG. 8E by the one-way traffic sign as an example.

A frontage road is classified as a subsidiary road that is parallel to ahighway and provides unrestricted access to local destinations. Anexample frontage road 806 is schematically presented in FIG. 8F.

An on-ramp, also referred to as an entry ramp, is an interconnectingroadway of a traffic interchange that allows traffic to enter a highway,especially a limited access highway. FIG. 8G provides an example on-ramp808 from an undivided arterial road 810 a to a limited access highway812 a. In FIG. 8G, the on-ramp 808 includes the length from a first gorepoint 810 b on the undivided arterial road 810 a to a second gore point812 b on the highway 812 a. If roads are marked by dividing lines, theon-ramp starts from the clearance of the intersection on an undividedarterial road 810 a and ends at the dotted merge line on the highway 812a.

An off-ramp, or an exit ramp, is defined as an interconnecting roadwayof a limited access highway that allows a vehicle to exit the highway.As shown in FIG. 8H, an off-ramp 813 a usually starts from a gore point813 b on the highway and ends at a stop line (not shown) at the end ofthe ramp or a gore point in absence of the stop line.

As used herein, a gore area may generally refer to a triangular spacebetween the outside lane of a highway and an on-ramp or off-ramp. Thegore area can be defined by the white lines painted on the road thatmeet in a point where the highway and the on-ramp or off-ramp join, andis usually inside a hard boundary or a soft boundary and marked by a fewwhite lines, as schematically illustrated in FIG. 8I. A gore point maybe classified as the intersection of two map element lines or barriers.In FIG. 8G, 810 b is shown as a rounded intersection and 812 b is shownas a sharp intersection gore point.

An interchange is defined as a connecting roadway or a junction betweentwo highways. The interchange allows a vehicle to exit from one highwayat a first gore point, usually with traffic direction change, and toenter the next highway at a second gore point. The off-ramp and theon-ramp defined in the above paragraphs are examples of interchanges.

A merge area is an area that allows a vehicle to accelerate to thepresent flow of traffic and merge into the flow of traffic. The mergearea starts at a gore point 576 a where two solid white lines intersectand finishes at the point 576 b where the merged lane boundaries are thesame width as the surrounding lanes, as shown in FIG. 8J. A local mergearea is defined as an area in a local roadway that allows vehicles fromtwo lanes to merge into one lane. The local merge area starts at a gorepoint where two solid white lines intersect and finishes when the mergedlane boundaries are the same width as the surrounding lanes. In FIG. 8K,a local merge is indicated by traffic signs such as left lane ends andmerge right.

An emergency lane is defined as a road shoulder that is adjacent to thedriving lanes. FIG. 8L is a schematic of a roadway showing an emergencylane 814 c as part of a map extension 814 b. The emergency lane 814 c isoutside of a solid lane line 814 a, that marks the edge of the drivableroadway, and is bounded by a hard or soft roadside boundary 814 d. AnMRC lane is an emergency lane that can allow the autonomous vehicle 105to fulfill the first MRC maneuver that may include pulling the vehicleover to a shoulder of the roadway.

A part time shoulder lane is defined as an emergency lane that can beconverted to a driving lane during certain hours of the day. FIG. 8Mshows an example part time shoulder lane on a divided highway pointed byan arrow light 878. An evaculane is an emergency lane that can also beused as an additional driving lane during an evacuation. In FIGS. 8N-8Qare shown traffic signs indicating that an emergency lane can be used asan evaculane for different evacuation purposes. A temporary bus lane isan emergency lane that serves as a driving lane dedicated to buses.

An intersection is defined as an area where two or more roads converge,diverge, meet, or cross at the same elevation. In other words, at anintersection, multiple roads intersect the area, as shown in FIG. 8R asan example. If an intersection is regulated and controlled by stopsigns, the intersection can be referred to as a stop sign intersection.A stop sign intersection can have as many stop signs as the number ofthe converging drivable roadways, e.g., two-way, four-way, or etc. If anintersection is regulated and controlled by traffic signals, it is asignalized intersection or a traffic signal intersection. A trafficsignal intersection includes the area where all drivable roadways areintersecting. A traffic signal is defined as a light signal emitted froma device designed to control the flow of traffic. Moreover, if anintersection is regulated and controlled by yield signs, it is a yieldintersection. A yield intersection includes the area where all drivableroads are intersecting.

A T-intersection is classified as an intersection where one roadintersects with another at or close to right angle. A T-intersectionincludes the area where all three roadways intersect. A forkintersection is an intersection where one road splits into two or moreroads with traffic flowing in one direction. And a Y-intersection isdefined as an intersection where one road splits into two or more roadswith traffic flowing in both directions. The fork intersection and theY-intersection are differentiated by whether traffic flow is in onedirection. While a fork intersection has one directional flow oftraffic, a Y-intersection has two-way traffic.

A railroad crossing, also referred to as a level crossing, is defined asan intersection area where a railway intersects with a roadway forvehicles. A railroad crossing may include the width of the drivableroadway and the length between the stop lines in both directions.

A primary roadway is a roadway where a vehicle has a right-of-way at anintersection. And a second roadway is a roadway where a vehicle yields aright-of-way at an intersection.

A K-ramp is defined as a sequential combination of an on-ramp, atemporary merge lane, and an off-ramp, wherein the temporary merge isshorter than a predetermined distance. The predetermined distance canbe, for example, 1000 meters (3281 ft), although other values arepossible. The temporary merge lane is a lane between an on-ramp and anoff-ramp that does not end. Such a temporary merge lane is intended tobe used by a vehicle to change lane inward or to take the immediateoff-ramp to exit the highway, as shown in FIG. 8S.

A driving lane is a road space where vehicles drive under a specific setof rules. A managed lane is a type of highway lane that is operated witha management scheme, such as lane use restrictions or variable tolling,to optimize traffic flow or vehicle throughput, or both. A fast-tracktoll lane is an express lane with a fully electronic toll system withouta toll booth or traffic gate. Only a valid fast track account and aproperly mounted and set toll tag are required to use the express lanes.FIG. 8T and FIG. 8U provide two photos showing example express lanes asindicated by the traffic signs. A high-occupancy vehicle (HOV) lane,also known as a carpool lane, diamond lane, 2+ lane, transit lane, T2lane or T3 lane, is a traffic lane reserved for buses, or vehicleshaving two or more occupants. These restrictions may be imposed duringpeak travel times or may apply at all times. Many jurisdictions exemptother vehicles, including motorcycles, charter buses, emergency and lawenforcement vehicles, low-emission and other green vehicles for using anHOV lane. FIG. 8V shows an HOV lane entrance on a highway. Ahigh-occupancy toll (HOT) lane is defined as a lane that is available tohigh-occupancy vehicles and other vehicles that pay a toll to use. FIG.8W shows a HOT entrance toll station.

A center lane is defined as a single lane located in the middle of atwo-way street, in which traffic may travel in either direction or makea left turn. An example center lane 816 is schematically shown in FIG.8X. The center lane is also referred to as a two-way left turn lane, orTWLT lane.

A dynamic lane is defined as a lane in a roadway that is dynamic in itsset of rules based on a time of the day and traffic severity. Dynamiclane assignment strategies repurpose the dynamic lane road space basedon a current or expected demand conditions in order to improve theefficiency and safety of the transportation system. A dynamic lane caninclude reversible lanes on highways and arterials, merge (or junction)control on highway ramps, and part-time highway shoulder use.

A truck-only lane is defined as a lane designated for the use of trucksonly. A truck lane is shown in FIG. 8Y. A bicycle lane is a lane on alocal roadway that is sectioned off by dotted or solid lines and mayhave a bicycle symbol, as shown in FIG. 8Z. A bus lane is defined as adivision of a road marked with painted lines for use by buses, as shownin FIG. 8AA.

A slip lane is defined as a right turning lane that avoids an adjacentintersection. The slip lane bypasses the intersection by cutting aroundthe intersection and reaches directly to the target lane. A slip lane818 is schematically illustrated in FIG. 8AB, and another slip lane 820is shown in FIG. 8AC as an example.

A merge through lane is a straight lane that accepts mergers from amerge ending lane. The merge ending lane is defined as the lane that ismerging into the merge through lane, often from an on-ramp onto ahighway. A merge through lane 822 and a merge ending lane 824 are shownin FIG. 8AD.

A zipper lane is defined as a merge lane that has no right-of-waytaxonomy, e.g., when a lane is temporarily closed.

A hard boundary is defined a vertically elevated surface placed over aterrain. A curb is a type of hard boundary that separates a drivableroadway from non-drivable or pedestrian areas. Curbs are often foundadjacent to local drivable roadways that separate the emergency lanefrom the sidewalk.

A soft boundary is defined as a substantial road boundary that is not avertically elevated surface. Soft boundaries are usually flat boundariesthat, if crossed, would not directly lead to vehicle damage but is noton the drivable roadway and contains higher risk of unstable terrain anddamage due to debris or objects.

A sidewalk is a paved pedestrian area on the side of a local roadwaythat is separated from the drivable roadway. The sidewalk can extendfrom a curb to a hard or soft boundary. In FIG. 8AE, a sidewalk 826 isdefined between a curb 828 and a boundary 830, which can be either ahard boundary or a soft boundary. The sidewalk 826 is part of a mapextension 832.

A crosswalk is a pedestrian crossing area that intersects with adrivable roadway. The crosswalk area is classified by spaced horizontallines or zebra markings, vertical markers, or speed tables, and beginsat the curb or boundary on one side of the roadway and ends at the curbor boundary on the other side of the roadway. A crosswalk with spacedzebra markings is shown in FIG. 8AF.

An overpass is defined as a roadway that is vertically elevated overanother roadway. Such an overpass is vertically intersecting with theunderpass. On the other hand, a bridge is a structure carrying a road,path, railroad, or canal across a river, ravine, road, railroad, orother obstacle. An underpass is defined as a roadway that is verticallyunderneath another roadway. It is vertically intersecting with anoverpass.

The vertical clearance of any mapped, drivable area may be defined as toinclude at least the height of the highest point of the autonomousvehicle 105 plus a safety buffer. A tunnel is defined as an undergroundroadway that is enclosed except for the entrance and exit points. FIG.8AG shows an example of a tunnel with an entrance.

A curved road is defined as any roadway that is not longitudinallystraight. The radius of curvature for a curved road is usually 1,124meters (3,688 ft) or less. An inclined roadway is defined as any roadwaythat is not vertically level but with a slope.

Unclassified roadways are defined as those which do not fit any otherdefinition within this disclosure and are completely unfamiliar to theautonomous vehicle 105. Therefore, in the digital map stored in thememory 175 of the in-vehicle control computer 150, an unclassifiedroadway is un-defined and does not have a classification.

An unmarked roadway is defined as a roadway that does not consist oflane lines or markings to regulate the flow of traffic. An unmarked areaof a highway is defined as an area adjacent to the highway that isoutside of a hard or soft boundary of the emergency lane and is withinthe map boundary. Referring back to FIG. 8L, an unmarked area 814 e of ahighway is defined as the area outside of the boundary 814 d. A localunmarked area is defined as an area adjacent to a local roadway orsidewalk that is unpaved or unmarked. It is the space outside of a hardor soft boundary of the sidewalk, or outside of the curb in absence of asidewalk. Referring back to FIG. 8AE, a local unmarked area 834 isdefined as the area outside of the sidewalk 826 and the boundary 830.

A restricted driving area is any area on a roadway in which theautonomous vehicle 105 is not permitted to drive based on a regulation,control, or speed.

A driveway is defined as a restricted driving area that is theconnection of a local destination and a local roadway. A drivewayincludes the width between the curbs of the roadway and the length tothe destination and at least as long as the autonomous vehicle 105. Anexample driveway 580 is shown in FIG. 8AH, from a local roadway to thegas station in the photo.

A street parking is defined as the shoulder area adjacent to a localdriving lane dedicated to parking of vehicles. It is classified as thearea from the rightmost lane line, or the closest point of the parkingspace line in absence of a rightmost lane line, to the curb.

A construction zone is defined as an area in a drivable roadway that issectioned off for construction purposes, and is limited to the spaceinside of a virtual wall defined by traffic control devices and trafficsigns.

A no truck lane is defined as a lane that prohibits semi-trucks ortrucks to drive in. A no truck lane is shown in FIG. 8AI as indicated bythe arrow on the white traffic sign board.

A traffic sign is defined as a traffic control instruction givinginformation to road users. A speed limit is defined as the maximum speedat which road users, including vehicles, may travel on the designatedroadway.

A bump is defined as a temporary change in elevation of the roadway. Incertain embodiments, the bump has a length no longer than the length ofthe autonomous vehicle 105.

Besides roadways, in certain embodiments, the digital map covers areasextended beyond roadways. In certain embodiments, the map can extend byat least 6 meters (20 ft) laterally from the edges of the outermostdriving lanes. The map may further extend all on-ramps that intersectwith the highways that the autonomous vehicle 105 is configured to driveon by up to 300 meters (984 ft) in length. The map may extend allintersection cross traffic roadways by considering the amount of timethat the autonomous vehicle 105 will take to complete its maneuverthrough the intersection, the speed limit at the intersection, and asafety factor of, for example, 1.5. Therefore, in certain embodiments,Map Extension (m)=[Time of autonomous vehicle 105's intersectionmaneuver (s)]×[Speed limit at intersection (m/s)]×1.5.

Moreover, the map may extend all frontage roads that connect to thehighways that the autonomous vehicle 105 is configured to drive by up to300 meters (984 ft) in length and the entire width of the frontage road.The map may extend to the entire roadway of all pre-defined alternativeroutes.

In certain embodiments, the map extends to cover the entire roadway ofall off-route first MRC safety areas that do not vertically intersectwith the route plus an additional 400 meter (1312 ft) in length past theend of the last mapped MRC safety area. In certain embodiments, the mapmay extend the highways that the autonomous vehicle 105 is configured toexit through 400 meter past the nearest MRC safety area.

Roadway Types

When the autonomous vehicle 105 traverses on roadways, it relies on thehigh-definition digital map information or mapped information stored onthe in-vehicle control computer 150 and the global positioning unit onthe vehicle sensor subsystems 144 to guide its way. In certainembodiments, the autonomous vehicle 105 can continuously detect roadtype information with one or more camera, LIDAR and radar devicesinstalled on the vehicle and compare the real time road information,which forms a temporary map, with the mapped information stored on thein-vehicle control computer 150.

For example, the in-vehicle control computer 150 of the autonomousvehicle 105 may detect one way road traffic signs, including one way donot enter traffic signs, at a predetermined minimum distance, e.g., 200meters, 222 meters, 250 meters, 275 meters, or 300 meters beforeapproaching the target traffic signs. FIG. 8AJ and FIG. 8AK show exampleof “one way do not enter” and “one way” traffic signs. If the in-vehiclecontrol computer 150 determines that a one-way road is not mapped in thedigital map stored in the memory 175, it will take the temporary mapbased on the detected data as priority over the stored digital map andreport the detected information to the oversight system 350 via thewireless network 370 which may transfer the updated map information toother monitored autonomous vehicles 105.

The in-vehicle control computer 150 may make decisions based on thepre-developed plans stored on its in-vehicle control computer 150. Forexample, if the traffic direction of a one-way road according to trafficsign is in contradiction with the traffic direction stored in thepre-developed map, the control computer 150 may be configured to drivethe autonomous vehicle 105 to avoid entering that one-way road. If thein-vehicle control computer 150 detects that another vehicle is drivingin the wrong direction on a one-way road, it may decide to perform asafety critical lane change or trigger a first MRC stop if a shoulder isavailable.

In certain embodiments, the in-vehicle control computer 150 on theautonomous vehicle 105 can be configured to choose to drive on dividedroadways if they are available. The in-vehicle control computer 150 canbe configured to actively detect vehicles and obstacles present in alllanes of the divided roadway in the driving direction and can takeaction once an unfavorable situation occurs. If the roadway is anundivided roadway, the autonomous vehicle 105 can actively monitor thevehicles and obstacles present in all lanes of the undivided roadway inits driving direction and the opposite direction.

When the autonomous vehicle 105 approaches a fast-track toll lane, thein-vehicle control computer 150 may determine from the mappedinformation and the traffic signs perceived by the vehicle sensorsubsystems 144 whether the type (e.g., car, truck, etc.) of the vehiclethe autonomous vehicle 105 is allowed to drive in the lane. In someembodiments where the autonomous vehicle 105 is configured as a truck,certain fast-track toll lanes may not allow the truck to access thelane. In case the vehicle is not allowed, the in-vehicle controlcomputer 150 may search the map for alternative routes to continue itstrip to the destination. If the detected traffic signs and mappedinformation are in conflict about a fast-track toll lane, the in-vehiclecontrol computer 150 may consider which of the conflicting traffic signsare more prevalent or have a higher priority when making a determinationas to the nature of the fast-track lane.

When the in-vehicle control computer 150 of the autonomous vehicle 105detects a HOV or HOT lane traffic sign, it may determine from the mappedinformation and the detected traffic signs whether the lane or lanes arerestricted for high occupancy vehicles all the time. In the embodimentsin which the autonomous vehicle 105 belongs to a type that does notsatisfy the requirements to use the HOV or HOT lane, the in-vehiclecontrol computer 150 may plan an alternative route to continue itstravel to the destination. If the vehicle is allowed to use the lane(s),the in-vehicle control computer 150 may decide to drive on the lane(s).If the detected traffic signs and mapped data are in conflict about theHOV or HOT lane, the in-vehicle control computer 150 may consider whichof the conflicting traffic signs are more prevalent or have a higherpriority when making a determination as to the nature of the HOV or HOTlane.

In some embodiments when the autonomous vehicle 105 is a truck and arestricted truck lane traffic sign is detected, the in-vehicle controlcomputer 150 may find an alternative route to continue its journey tothe destination.

Roadway Surfaces

Road surfaces may be defined as rough, loose gravel, bridge (parallelstones or grated surfaces), slippery surface types, etc. In certainembodiments, the digital map stored in memory 175 of the in-vehiclecontrol computer 150 of the autonomous vehicle 105 may include roadsurface metadata on roadway segments. The digital map may also containundrivable road statistics on road surfaces. Undrivable road statisticsare defined as the number of failures detected by the autonomousvehicles 105 connected together by the wireless network 370 attemptingto use the road. For example, upon approaching a roadway, the in-vehiclecontrol computer 150 may check the high-definition digital map metadatato verify the conditions of the roadway, including the undrivablestatistics. If the roadway is identified as undrivable, the in-vehiclecontrol computer 150 may develop a plan to avoid the roadway ifpossible.

Besides checking mapped road surface condition data, the in-vehiclecontrol computer 150 may use the vehicle sensor subsystems 144 equippedon the autonomous vehicle 105 to detect or observe road signs for roadsurface conditions. For example, the in-vehicle control computer 150 maydetect unpaved road and pavement end road signs at a predeterminedminimum distance, e.g., greater than 200 meters, 250 meters, 300 meters,or 350 meters, before reaching the road signs. The in-vehicle controlcomputer 150 may detect uneven road surface signs at a predeterminedminimum distance, e.g., greater than 200 meters, 222 meters, 250 meters,or greater than 275 meters, before approaching the traffic signs. FIG.8AL and FIG. 8AM show examples of uneven road surface traffic signs. Inaddition, the in-vehicle control computer 150 may detect auxiliary stopsides using road signs at a predetermined minimum distance, e.g., 200meters, 222 meters, 250 meters, or 275 meters, before approaching theroad signs, as shown in FIG. 8AN, which is a soft shoulder road sign, asan example. The in-vehicle control computer 150 may detect road surfacesusing horizontal and vertical markings or traffic signs at apredetermined minimum distance, e.g., greater than 200 meters, 222meters, 250 meters, or greater than 275 meters, before approaching thetraffic signs. FIGS. 8AO-5AR are examples of traffic signs for roadsurface conditions. Furthermore, the autonomous vehicle 105 may detectthe road signs for rocks falling on road at a predetermined minimumdistance, e.g., greater than 200 meters, 222 meters, 250 meters, orgreater than 275 meters, before approaching the traffic signs. FIG. 8ASshows an example rocks-on-road traffic sign.

When an unfavorable road condition is detected by the in-vehicle controlcomputer 150 in conjunction with the vehicle sensor subsystems 144 fromreading a road sign, the computer may take actions according to thepre-developed algorithms for selecting alternative routes or to minimizethe negative impact.

For example, if rocks are detected on a road, the autonomous vehicle 105may stop and plan an alternative route to go around the rocks. If anuneven road is detected, the autonomous vehicle 105 may decide that theuneven road is not safe and plan an alternative road to avoid the unevenroad. The in-vehicle control computer 150 may also plan an alternativeroute to avoid a pavement end or unpaved road because an unpaved roadmay affect the performance behavior of the autonomous vehicle 105.Unpaved roads can create dust for following vehicles and impair roadvisibility. Once on an unpaved road, the autonomous vehicle 105 mayavoid overtaking vehicles in front for safety measures. For example, theautonomous vehicle 105 can prioritize paved lanes when planning a routeto destination.

The autonomous vehicle 105 may change speed depending on the roadsurface conditions. For example, when loose gravel, slippery or roughsurfaces are detected for a road segment, the autonomous vehicle 105 mayslow down by a predetermined percentage of speed, e.g., by 10%, 15%, or20% of the speed limit. The autonomous vehicle 105 may plan a lanechange or overtaking other vehicles that have sudden movements. In someembodiments, when the autonomous vehicle 105 is a truck, suddenmovements can impact the movement of its cargo/load and change thecenter of gravity of the vehicle. This can increase lateral accelerationand may result in the vehicle crossing the intended lane boundaries.

In other embodiments, if the autonomous vehicle 105 is a truck, thein-vehicle control computer 150 of the vehicle may develop a plan toavoid a drawbridge or bridge lanes with grated surfaces, because gratedsurfaces may not support heavy trucks. When planning a stop, theautonomous vehicle 105 may use the unpaved soft shoulders in case it isnot allowed to stop on the road.

Mapped Construction Zone

In a construction zone, things change progressively and dynamically, sothe information in the stored map may not accurately reflect the currentroad conditions. For example, detection of the current road conditionsby the vehicle sensor subsystems 144 becomes important for the safety ofthe autonomous vehicle 105 and other road users. There are differenttypes of traffic signs for construction zones. And a sign may be held bya person with hand signals to transfer different meanings to theoncoming vehicles. The autonomous vehicle 105 considers a personcontrolling traffic in a construction zone area as a construction zoneflagger. In FIG. 8AT and FIG. 8AU, two construction zone flaggers areshown as examples, each of them holding a stop/slow signaling paddle. Aconstruction zone flagger may use a “stop road users” hand signal tostop vehicles. To do this, the construction zone flagger faces theoncoming traffic and aims a stop paddle held in one hand toward thetraffic in a stationary position. The construction zone flagger can holdthe free hand above the shoulder level with the palm open and toward theapproaching traffic, as shown in FIG. 8AV. To allow the traffic toproceed through the construction zone, the construction zone flaggeruses a “proceed road users” hand signal. The construction zone flaggerfaces the oncoming traffic and aims a slow paddle toward the traffic ina stationary position. Meanwhile his/her free arm extends horizontallyaway from the body and motions with the free hand for the traffic tomove forward, as shown in FIG. 8AW and FIG. 8AX. Another signal is a“slow road users” hand signal. In this case, the construction zoneflagger faces the oncoming traffic and aims the slow paddle toward thetraffic in a stationary position. At the same time his/her free armextends horizontally away from the body and moves the free hand up anddown with palm down, as shown in FIG. 8AY and FIG. 8AZ.

Since construction zones are progressive and evolving, the autonomousvehicles 105 may actively detect changes with the vehicle sensorsubsystems 144, update the digital map, and periodically inform theoversight system 350 through the wireless network 370. The updatedinformation may include construction zone traffic signs, speed limittraffic signs, lane closure traffic signs, and etc. The autonomousvehicle 105 may detect construction zone traffic signs at apredetermined distance, e.g., at 200 meters, 300 meters, or 400 metersfrom the traffic signs. The types of construction traffic signs may beconstruction zone signs, as shown in FIGS. 8BA-8BD, the lane closuresigns, as shown in FIGS. 8BE-8BI, the detour signs, as shown in FIG.8BJ, the speed limit signs, as shown in FIGS. 8BK-8BM, and the end ofconstruction zone signs, as shown in FIGS. 8BN-8BP. The constructionzone traffic signs in the above-mentioned figures are shown as examples.The road devices detected by the autonomous vehicle 105 may includetraffic control devices as well. The in-vehicle control computer 150 ofthe autonomous vehicle 105 may be able to detect the differences of whatis perceived from the sensors from what is in the stored map, andtransmit the collected real time data to the oversight system 350 overthe wireless network 370 once a difference is detected, so that thedigital map maintained by the oversight system 350 gets updated. Exceptthe types and locations of the construction zone signs, the real timedifferences may include lane closure information, speed limitinformation and lane shift information.

The autonomous vehicle 105 may be able to detect the construction zoneflagger's hand signal devices, as shown in FIGS. 8BQ-8BS, and alsodetect and interpret the construction zone flagger's hand signaling,including the “stop road users” hand signal, the “proceed road users”hand signal, and the “slow road users” hand signal, as illustrated inFIGS. 8BT-8CB. In case that the autonomous vehicle 105 cannot interpreta hand signal from a construction zone flagger, it may decide to stop atleast a predetermined distance, e.g., 4 meters, 5 meters, 6 meters, 7meters, or 8 meters, away from the flagger, although other distances arealso possible. The autonomous vehicle 105 may inform the oversightsystem 350 of the information.

Upon approaching a construction zone according to the map or thedetection of the construction zone traffic signs, the in-vehicle controlcomputer 150 of the autonomous vehicle 105 may decide plans and takeactions according to the pre-developed algorithms to minimize thenegative impact.

For example, when approaching a mapped construction zone, the autonomousvehicle 105 may maintain a predetermined safe distance from trafficcontrol devices. The predetermined safe distance can include, forexample, 8%, 10%, or 12% of lane width. When a construction zone trafficsign without a speed limit is detected, the autonomous vehicle 105 mayslow down by a margin from its initial speed. The margin can range from5 MPH to 10 MPH in certain embodiments. When a construction zone speedlimit sign is detected, the autonomous vehicle 105 may follow speedlimit regulation given by the relevant traffic sign.

If the autonomous vehicle 105 detects a construction zone traffic signindicating headlight-on, the vehicle may turn on its headlights.

The in-vehicle control computer 150 may actively sense for constructionzone flaggers and observe hand signals. If the detected hand signal is a“stop road users”, the autonomous vehicle 105 may plan to stop at apredetermined minimum distance, e.g., at least 5 meters, 6 meters, or 7meters, from a construction zone flagger. If the hand signal is a “slowroad users”, the autonomous vehicle 105 may slow down by a margin fromthe initial speed, e.g., the margin ranging from 10 MPH to 20 MPH. Ifthe hand signal is a signal for the road users to proceed, theautonomous vehicle 105 may plan to proceed forward.

When a detour traffic sign is detected, the autonomous vehicle 105 mayfollow the direction indicated by the detour traffic sign. When aconstruction zone lane closure sign is detected, the autonomous vehicle105 may plan a critical safety lane change to a lane that is not closedat least a predetermined distance, e.g., 50 meters, 100 meters, or 150meters, ahead of the lane closure.

In certain situations, the in-vehicle control computer 150 of theautonomous vehicle 105 may decide to send the detected information tothe oversight system 350 over the wireless network 370. Whenconstruction zone traffic signs are detected, the autonomous vehicle 105may inform the oversight system 350. When an end of construction zonesign is detected, the autonomous vehicle 105 may also inform theoversight system 350. And the autonomous vehicle 105 may periodicallyinform the oversight system 350 if there is any change of verticalclearance height within the construction zone. In addition, theautonomous vehicle 105 may continuously inform the oversight system 350if there is any change of weight limit within the construction zone.

Furthermore, the autonomous vehicle 105 may inform the oversight system350 if the mapped information is in conflict with the perceivedinformation. For example, when a mapped construction zone is notdetected, the autonomous vehicle 105 may inform the oversight system350. In some embodiments, when a conflict is detected between the mappedand perceived speed limit, the autonomous vehicle 105 may follow theminimum of the regulated speed limit. When a conflict is detectedbetween the mapped and perceived lane shift, the autonomous vehicle 105may inform the oversight system 350. These conflicts can be categorizedas a lane closure and lane shift.

Lane Restriction Navigation

When the autonomous vehicle 105 travels from a starting point to adestination, the in-vehicle control computer 150 may continuouslyinquire on positioning and road condition data from the map stored inthe memory 175 on the in-vehicle control computer 150, and develop plansand take actions for the next segments of roadways based on thepre-developed algorithms. However, the in-vehicle control computer 150may also control the vehicle sensor subsystems 144, including theplurality of camera, radar and LIDAR devices mounted on the vehicle, tocontinuously monitor the road conditions. Upon discovery of a differencebetween the mapped road data and detected road data, the in-vehiclecontrol computer 150 may update the temporary map with the detected roaddata and transmit the detected data to the oversight system 350 over thewireless network 370.

For example, the in-vehicle control computer 150 may periodically (e.g.,continuously in some embodiments) inform the oversight system 350 of thelocation of HOV lanes, narrow lanes and lane splitting when the detectedroad information is in conflict with the mapped road data. Other aspectsof the detected road data in conflict with mapped data may include anyweight limitations on the lane, e.g., as shown in FIGS. 8CC-8CG, if theautonomous vehicle 105 is a truck, and the details of ongoing roadconstruction, e.g., as shown in FIGS. 8CH-8CQ.

While the autonomous vehicle 105 actively detects and compares roadconditions with the data retrieved from the stored map, the autonomousvehicle 105 may focus on certain aspects of the roadways. For example,the autonomous vehicle 105 may detect a narrow lane in advance, or at apredetermined distance, e.g., at least 200 meters, 222 meters, or 250meters before arriving at the narrow lane. A road narrowing traffic signis shown in FIG. 8CR. A narrow lane may be defined as a lane narrowerthan or equal to a predetermined width limit, e.g., 3.25 meters, 3.35meters, or 3.5 meters wide. The autonomous vehicle 105 may detect theroad signs with lane restriction information before approaching the lanerestriction location. Examples of road restriction signs are shown inFIGS. 8CS-BCU.

In addition, the autonomous vehicle 105 may detect traffic controldevices, e.g., cones, lighting devices, markings, barricades, securitypersonals, and etc. A few examples of traffic control devices are shownin FIGS. 8CV-8CZ. The autonomous vehicle 105 may establish a virtualwall based on the detected traffic control devices and traffic signsaround a construction zone at a predetermined distance, e.g., at least200 meters, 222 meters, or 250 meters, before arriving at theconstruction zone. The autonomous vehicle 105 may detect a yield sign ora merge sign at a distance that is longer than the deceleration distanceof the autonomous vehicle. An example merge sign is shown in FIG. 8DA.

In addition, in the embodiments when the autonomous vehicle 105 is atruck, the in-vehicle control computer 150 may detect if an availableHOV lane allows trucks to pass through.

The autonomous vehicle 105 may develop plans according to the detectedroad information. For example, when the in-vehicle control computer 150detects a lane restriction traffic sign, the autonomous vehicle 105 mayplan a lane change at a predetermined distance, e.g., at least 200meters, 222 meters, or 250 meters before arriving at the restrictionlocation. When the in-vehicle control computer 150 detects a lane mergetraffic sign, the vehicle may plan to merge into the available lane at apredetermined distance, e.g., at least 200 meters, 222 meters, or 250meters before arriving at the merging spot.

Example Technique for Updating the Digital Map Based on Real Time Datafrom the Autonomous Vehicle

In certain embodiments, the digital map stored in the memory 175 on thein-vehicle control computer 150 of the autonomous vehicle 105 waspre-developed. In certain embodiments, the digital map is continuouslyor periodically updated with real time data collected by the perceptionsensor on the autonomous vehicle 105. FIG. 8DB illustrates an examplemethod which can be used to update the digital map.

The method 850 begins at block 852. At block 854, the in-vehicle controlcomputer, or the processor, 150 is configured to receive perception datafrom at least one perception sensor of the autonomous vehicle 105. Atblock 856, the in-vehicle control computer 150 is configured to generateroadway condition data based on the perception data.

At block 858, the in-vehicle control computer 850 is configured toreceive global positioning system (GPS) data from the GPS receivingdevice of the autonomous vehicle 105. At block 860, the in-vehiclecontrol computer 150 is configured to retrieve mapped data from thememory, or a non-transitory computer readable medium, 175 on thein-vehicle control computer 150 of the autonomous vehicle 105.

Then at block 862, the in-vehicle control computer 150 is configured todetermine that the GPS data meets a minimum localization accuracyrequirement. When it is determined that the GPS data meets the minimumlocalization accuracy requirement, at block 864, the in-vehicle controlcomputer 150 is configured to combine the generated road condition datawith the GPS data to form detected road data.

At block 866, the in-vehicle control computer 150 is configured todetermine that there is a discrepancy between the detected road data andthe retrieved mapped data. If a discrepancy between the detected roaddata and the retrieved mapped data is determined, at block 868, thein-vehicle control computer 150 is configured to update the mapped datawith the detected road data, and send the updated mapped data to theremote oversight system through the network communication subsystem.Then the method comes to an at end block 870.

Physical Infrastructure

The autonomous vehicle 105 can be configured to perform a number ofdifferent tasks related to the physical infrastructure on or near theroadway. Examples of physical infrastructure that the autonomous vehicle105 can be configured to detect and respond to include: servicestations, terminals (e.g., autonomous freight network terminals), lowvertical clearance areas, weigh stations, toll booths, ramp meters,bumps, emergency lanes, roadway widths, and tunnels. In situations wherethe autonomous vehicle 105 is out of ODD, it can also be important forthe autonomous vehicle 105 to perform an MRC. The physicalinfrastructure can also determine whether an MRC is possible and how theautonomous vehicle 105 should be controlled during an MRC.

In certain embodiments, the autonomous vehicle 105 can determine aminimal risk condition (MRC) maneuver for the autonomous vehicle 105 toexecute, identify a safe zone in which the autonomous vehicle 105 isable to execute the MRC maneuver by coming to a stop based on perceptiondata, identify one or more exclusion zones within the safe zone based onthe perception data, and control the autonomous vehicle 105 to executethe MRC maneuver including stopping outside of the exclusion zone.

Minimal Risk Condition (MRC) Maneuvers

One important aspect involved in safely navigating an autonomous vehicle105 is the ability of the autonomous vehicle 105 to perform an MRCmaneuver when it is no longer safe for the autonomous vehicle 105 tocontinue navigation along its current route (e.g., the autonomousvehicle 105 is out of ODD). One aspect involved in successfullyexecuting an MRC maneuver is detecting the local environment of theautonomous vehicle 105. For example, it is desirable that the autonomousvehicle 105 is able to identify regions in which the autonomous vehicle105 can drive and come to a stop while performing the MRC maneuver, aswell as regions in which the autonomous vehicle 105 cannot enter duringan MRC maneuver.

In some embodiments, the in-vehicle control computer 150 is configuredto establish exclusion zones defining a minimum distance that theautonomous vehicle 105 is configured to stay away from any emergencylane vehicles (ELVs). In some embodiments, the exclusion zones may beincluded as part of and/or overlap with safe zones (e.g., a shoulder ofthe roadway).

For example, the in-vehicle control computer 150 can define theexclusion zones to include all areas that are within the predetermineddistance in front of or behind any ELV. The predetermined distance caninclude, for example, 100 meters, although other values are alsopossible without departing from this disclosure. The in-vehicle controlcomputer 150 is configured to avoid entering the exclusion zones whileperforming any MRC maneuver, including the first MRC maneuver.Advantageously, by using exclusion zones, the in-vehicle controlcomputer 150 is configured to provide additional risk mitigation toavoid collisions while performing MRC maneuvers.

The in-vehicle control computer 150 can also be configured to avoidcompleting an MRC maneuver (e.g., avoid stopping the autonomous vehicle105 after completing an MRC maneuver) when within a threshold distancefrom a crosswalk at an intersection. The threshold distance can include,for example, 20 feet, although other values are also possible.Advantageously, avoiding completing an MRC maneuver within a thresholddistance from a crosswalk can improve safety and risk mitigation toavoid pedestrians while performing the MRC maneuver.

In some embodiments, the in-vehicle control computer 150 can further beconfigured to avoid completing an MRC maneuver within a thresholddistance from a fire hydrant. The threshold distance can include, forexample, 4.6 meters (15 feet), although other values are also possible.Because parking next to a fire hydrant is against the law, theautonomous vehicle 105 can be configured to avoid completing an MRCmaneuver within the threshold distance from a fire hydrant whenpossible.

The in-vehicle control computer 150 can also be configured to avoidcompleting an MRC maneuver within a threshold distance from a flashingbeacon or traffic control signal located on the side of the roadway. Thethreshold distance can include, for example, 9.1 meters (30 feet),although other values are also possible. Parking next to a flashingbeacon may be against the law and thus the autonomous vehicle 105 can beconfigured to avoid completing an MRC maneuver within the thresholddistance from a flashing beacon or traffic control signal.

In some embodiments, the in-vehicle control computer 150 can also beconfigured to avoid completing an MRC maneuver within a thresholddistance from a railroad crossing/track. The threshold distance caninclude, for example, 50 feet, although other distances are alsopossible. Parking next to a railroad track may be against the law, andthus, the autonomous vehicle 105 can be configured to avoid completingan MRC maneuver within the threshold distance from a railroad track. Inorder to ensure safety of the autonomous vehicle 105 as well as anynearby road users, the autonomous vehicle 105 is configured to avoidparking on a railroad track for any reason.

When the autonomous vehicle 105 performs an MRC maneuver, the in-vehiclecontrol computer 150 can be configured to provide visual and/or audionotification(s) to an in-cab human machine interface (HMI). For example,the notification can contain information about the type of MRC initiatedin response to initiating and/or completing the MRC maneuver. When adriver is present inside the cab of the autonomous vehicle 105, visualand audio notifications can be important for driver feedback as to theautonomous vehicle's 105 intended MRC maneuver.

The in-vehicle control computer 150 can also be configured to providenotification(s) to the oversight system 350 in response to initiatingand/or completing the MRC maneuver. For example, the notification cancontain information about the type of MRC initiated in response toinitiating and/or completing the MRC maneuver.

In some situations, a chase vehicle may be following the autonomousvehicle 105. In these situations, it can be important for the safety ofthe autonomous vehicle 105 to provide information regarding any MRCmaneuvers that the autonomous vehicle 105 will or is executing. Thus,the in-vehicle control computer 150 can also be configured to providethe notification(s) to the chase vehicle directly and/or via theoversight system 350.

In some embodiments, the in-vehicle control computer 150 can beconfigured to provide notification(s) on an exterior of the autonomousvehicle 105 in response to initiating and/or completing the MRCmaneuver. For example, the in-vehicle control computer 150 can beconfigured to activate the autonomous vehicle's 105 hazard lights thatare externally visible. In some embodiments, the in-vehicle controlcomputer 150 can be configured to activate the hazard lights in responseto reaching the furthest right lane during the first MRC maneuver andkeep the hazard lights on after completing the first MRC maneuver. Byactivating the hazard lights, the autonomous vehicle 105 can notifyother road users that that autonomous vehicle 105 is stopped or willsoon stop and to help notify the chase vehicle and personnel of theautonomous vehicle's 105 location.

In certain implementations, the in-vehicle control computer 150 can alsobe configured to turn on the hazard lights once the autonomous vehicle105 has reached the furthest right driving lane while performing a firstMRC maneuver. The in-vehicle control computer 150 can be configured tokeep the hazard lights on until a human operator turns the hazard lightoff. In some embodiments, during a first MRC maneuver, the usage of theautonomous vehicle's 105 turn indicator for lane changes between drivinglanes supersedes the usage of hazard lights. Thus, the autonomousvehicle 105 can inform other road users of the intended lane changesperformed during the first MRC maneuver. Hazard lights can be importantnotifications to other road users to notify the road users that theautonomous vehicle 105 is performing a safety maneuver to get to a safearea.

When executing an MRC maneuver, the in-vehicle control computer 150 canbe configured to maintain a minimum lateral distance between the outermost point of the autonomous vehicle 105 and the lane line thatseparates the driving lane from the non-driving lane at the closestpoint of approach when the autonomous vehicle 105 comes to a stop. Theminimum lateral distance can include, for example, 0.3 m although othervalues are also possible. The separation of the autonomous vehicle 105to the lane line when the autonomous vehicle 105 comes to a stop canprovide drivers and passengers of the autonomous vehicle 105 a safe exitwhile not intruding on any driving lane.

In some embodiments, when executing the first MRC maneuver, thein-vehicle control computer 150 can be configured to control theautonomous vehicle 105 to come to a stop when the autonomous vehicle 105is completely within the pre-mapped safety area and no part of theautonomous vehicle 105 is intruding into the driving lane. This can beimportant for the safety of the autonomous vehicle 105 and other roadusers so that the autonomous vehicle 105 is fully in the shoulder andnot blocking oncoming traffic as well as provide the space necessary forany driver or passenger to get out of the autonomous vehicle 105.

In some embodiments, the in-vehicle control computer 150 can beconfigured to define exclusion zones of construction zones. For example,the in-vehicle control computer 150 can define areas that are within athreshold distance from a construction zone as exclusion zones. Thethreshold distance can include, for example, 100 meters although othervalues are also possible. The autonomous vehicle 105 can be configuredto avoid entering any exclusion zones. Construction zones can presentthe risk of pedestrians being present as well as debris that can bedamaging to the autonomous vehicle 105.

The in-vehicle control computer 150 can also be configured to avoidcompleting an MRC maneuver within an intersection. For example, it canbe important for the safety of the autonomous vehicle 105 and other roadusers to avoid blocking traffic in intersections.

The in-vehicle control computer 150 can also be configured to avoidcompleting an MRC maneuver on a bridge. For example, performing orcompleting an MRC maneuver on a bridge can present a safety risk to theautonomous vehicle 105 and other road users and thus can be avoided toimprove safety.

The in-vehicle control computer 150 can also be configured to avoidcompleting an MRC maneuver inside of a tunnel. Performing an MRCmaneuver inside of a highway tunnel or other tunnel can present multiplesafety risks and thus can be avoided to improve safety.

The in-vehicle control computer 150 can also be configured to avoidcompleting an MRC maneuver within an interchange. Interchanges oftenhave curved roads, which can present a challenge for the autonomousvehicle 105 to come to a stop fully outside of the driving lanes, whichcan lead to a safety risk.

The in-vehicle control computer 150 can further be configured to avoidcompleting an MRC maneuver at a location where roadway signs prohibitstanding or stopping. For example, completing a first MRC maneuver at aprohibited stopping zone may be against the law and thus should beavoided.

While performing an MRC maneuver, the in-vehicle control computer 150can be configured to yield to any approaching emergency vehicle that hasactivated its siren and/or emergency lights. The in-vehicle controlcomputer 150 can further be configured to prioritize a passing emergencyvehicle higher than many or most other actions, and thus, the in-vehiclecontrol computer 150 can be configured to yield to emergency vehiclesduring an MRC maneuver for safety reasons.

The in-vehicle control computer 150 can also be configured to receive aninput from a driver behind the wheel of the autonomous vehicle 105 tocancel an MRC maneuver. A driver takeover can improve safety by enablingthe driver to control the autonomous vehicle 105 when the driver hasdetermined that performing the MRC maneuver is not necessary and/orwould impact safety for the autonomous vehicle 105 or other road users.

The in-vehicle control computer 150 can also be configured to receive aninput from the oversight system 350 to trigger an MRC maneuver. Byproviding for oversight system 350 control, the in-vehicle controlcomputer 150 can improve safety in cases where an MRC maneuver isdesirable but may not have been triggered by the autonomous vehicle 105itself.

In some embodiments, the in-vehicle control computer 150 can also beconfigured to maintain the autonomous vehicle 105 at a minimum speedwhen entering a safe area during an MRC maneuver. The minimum speed caninclude, for example, 30 mph, although other minimum speeds are alsopossible. The in-vehicle control computer 150 can be configured tomaintain the autonomous vehicle 105 at the minimum speed when entering asafe area during an MRC maneuver unless certain external circumstancesprevent the autonomous vehicle 105 from doing so. By maintaining theminimum speed, for example on a freeway, the autonomous vehicle 105 canprevent certain types of safety risks to any vehicles located behind theautonomous vehicle 105 and to provide the speed necessary for theautonomous vehicle 105 to complete any lane changes associated with theMRC maneuver.

In some embodiments, the in-vehicle control computer 150 can also beconfigured to maintain the autonomous vehicle 105 at maximum speed whenentering a safe area during an MRC maneuver. The maximum speed caninclude, for example, 50 mph although other maximum speeds are alsopossible. Based on the length parameter associated with many safe zones,it is desirable to set a maximum speed such that the autonomous vehicle105 is able to come to a complete stop within the length of the safezone.

The in-vehicle control computer 150 can also be configured to classifylane changes triggered based on performing a first MRC maneuver to be anon-critical safety maneuver. Thus, the in-vehicle control computer 150can also be configured to deny lane changes due to other actions (e.g.,safety deniers) with priorities equal or higher than non-critical safetylane changes. The in-vehicle control computer 150 can also be configuredto have an exception such that the lane change cannot be denied by aslow moving vehicle. In some implementations, the in-vehicle controlcomputer 150 may not classify the first MRC maneuver as a safetycritical maneuver because the autonomous vehicle 105 may still yield foremergency vehicles and prioritize critical safety deniers.

The in-vehicle control computer 150 can also be configured to trigger afirst MRC maneuver in response to determining that the autonomousvehicle 105 is approaching the boundary of the map. The in-vehiclecontrol computer 150 can also be configured to execute the first MRCmaneuver in response to approaching the boundary of the map as long as asafe zone can be located within the map boundary. In someimplementations, the in-vehicle control computer 150 can also beconfigured to avoid the autonomous vehicle 105 reaching the map boundaryfor safety reasons.

FIG. 9A illustrates an example visualization of a first MRC maneuver foran example scenario where there are no external complications. In oneexample scenario in which there are no external complications, thein-vehicle control computer 150 can also be configured to identify anissue that triggers the first MRC, send visual and audio notification incabin and on the HMI, turn on the autonomous vehicle's 105 turn signals,and initiate the first MRC command. The in-vehicle control computer 150can also control the autonomous vehicle 105 to perform one lane changeat a time, lane change into a far right lane, and turn on the hazardlights. The in-vehicle control computer 150 can further search for thenearest MRC safe zone, verify that the MRC safe zone is not an exclusionzone and is not blocked, pull the autonomous vehicle 105 over to thesafe zone, and control the autonomous vehicle 105 to come to a completestop within safe zone boundaries. Thereafter, the in-vehicle controlcomputer 150 can also be configured to control the autonomous vehicle105 to keeps the hazard lights on and send notifications to the in-cabinHMI and to the oversight system 350.

FIG. 9B illustrates an example visualization of a first MRC maneuver fora first example scenario in which the autonomous vehicle 105 isperforming a left lane change. FIG. 9C illustrates an examplevisualization of a first MRC maneuver for a second example scenario inwhich the autonomous vehicle 105 is performing a left lane change. Thein-vehicle control computer 150 can be configured to cancel a left lanechange when performing a first MRC maneuver, except if the left lanechange is classified as a critical lane change.

In the first example scenario, the in-vehicle control computer 150 caninitiate a left lane change. The in-vehicle control computer 150 cantrigger a first MRC maneuver, assess localization of the autonomousvehicle 105 with respect to the lane lines, and verify that the centroidof the autonomous vehicle 105 has not crossed the lane line into thetarget lane of the left lane change. In response to verifying that thecentroid of the autonomous vehicle 105 has not crossed the lane lineinto the target lane, the in-vehicle control computer 150 can abort alane change (unless the lane change is classified as critical), returnthe autonomous vehicle 105 back to its current lane, complete the lanechange abortion, and initiate the first MRC maneuver.

In the second example scenario, the in-vehicle control computer 150 caninitiate a left lane change. The in-vehicle control computer 150 cantrigger a first MRC maneuver. The in-vehicle control computer 150 canassess localization of the autonomous vehicle 105 with respect to thelane lines and verify that the centroid of the autonomous vehicle 105has crossed the lane line into the target lane. In response to verifyingthat the centroid of the autonomous vehicle 105 has crossed the laneline into the target lane, the in-vehicle control computer 150 cancontrol the autonomous vehicle 105 to complete lane change and initiatethe first MRC maneuver.

FIG. 9D illustrates an example visualization of a first MRC maneuver foran example scenario in which the autonomous vehicle 105 is performing aright lane change. Since a right lane change moves in the direction ofthe first MRC maneuver, the in-vehicle control computer 150 can beconfigured to avoid cancelling the lane change before triggering thefirst MRC maneuver. In the example scenario, the in-vehicle controlcomputer 150 can initiate a right lane change, trigger a first MRCmaneuver, control the autonomous vehicle 105 to complete the lanechange, and initiate the first MRC maneuver.

FIG. 9E illustrates an example visualization of a first MRC maneuver foran example scenario in which the safe area is taken or occupied. Whenthe safe area is taken or otherwise obstructed, the autonomous vehicle105 may not be able to use the safe area and the in-vehicle controlcomputer 150 can mark the safe area as blocked. In one example scenario,the in-vehicle control computer 150 can trigger a first MRC maneuver,initiate the first MRC maneuver, and control the autonomous vehicle 105to change lanes into the far right lane. The in-vehicle control computer150 can also search for the nearest MRC safe zone, locate the nearestMRC safe zone, verify that the MRC safe zone is blocked, and continuecontrolling the autonomous vehicle 105 to drive to the next safe zone.

FIG. 9F illustrates an example visualization of a first MRC maneuver foran example scenario in which an ELV is located in the safe zone. Thein-vehicle control computer 150 can be configured to apply restrictionsand design goals with respect to the proximity of the autonomous vehicle105 to an ELV when completing a first MRC maneuver and pulling over intoa safe zone. The restrictions and design goals generally refer to aminimum distance from the autonomous vehicle 105 to the ELV for theautonomous vehicle 105 to be able to use the safe zone in which the ELVis located. As described herein, the autonomous vehicle 105 isconfigured to come to a complete stop with a sufficient distance betweenthe autonomous vehicle 105 and the ELV. In one example scenario, thein-vehicle control computer 150 can trigger a first MRC maneuver,initiate the first MRC maneuver, and control the autonomous vehicle 105to change lanes into the far right lane. The in-vehicle control computer150 can search for the nearest MRC safe zone, locate the nearest MRCsafe zone, and verify that an ELV is in the safe zone. The in-vehiclecontrol computer 150 can verify that there is sufficient space to pullinto the safe zone and control the autonomous vehicle 105 to completethe first MRC maneuver at least a predetermined distance (e.g., 100 m)away from an ELV.

FIG. 9G illustrates an example visualization of a first MRC maneuver foran example scenario in which the autonomous vehicle 105 is approaching amap boundary. For example, the in-vehicle control computer 150 can beconfigured to determine that the autonomous vehicle 105 is approaching amap boundary, and in response, determine that the autonomous vehicle 105should execute the first MRC maneuver. In one example scenario, thein-vehicle control computer 150 can determine that the autonomousvehicle 105 is approaching a map boundary, assess route options, verifythat the autonomous vehicle 105 will hit the map boundary, and searchfor the nearest safe zone. The in-vehicle control computer 150 canlocate the nearest safe zone, verify that the safe zone is within themap boundary, and trigger the first MRC maneuver. The in-vehicle controlcomputer 150 can initiate the first MRC maneuver, control the autonomousvehicle 105 to complete the first MRC maneuver, and contact theoversight system 350.

FIG. 9H illustrates an example visualization of a first MRC maneuver foran example scenario in which the autonomous vehicle 105 has missed anexit. When the autonomous vehicle 105 has missed its exit, theautonomous vehicle 105 may determine that performing a first MRCmaneuver is the correct course of action. In one example scenario, thein-vehicle control computer 150 can determine that the autonomousvehicle has missed its exit, attempt to reroute the autonomous vehicle105, and determine that there is not a feasible reroute to thedestination. The in-vehicle control computer 150 can trigger the firstMRC maneuver, initiate the first MRC maneuver, control the autonomousvehicle 105 to complete the first MRC maneuver, and contact theoversight system 350.

FIG. 9I illustrates an example visualization of a first MRC maneuver foran example scenario in which the autonomous vehicle 105 has been forcedoff route. The autonomous vehicle 105 may be forced off route for avariety of reasons including most commonly due to road closures. In oneexample scenario, the in-vehicle control computer 150 can determine thatthe autonomous vehicle 105 is forced off route, attempt to reroute theautonomous vehicle 105, and determine that there is not a feasiblereroute to the destination. The in-vehicle control computer 150 cantrigger a first MRC maneuver, initiate the first MRC maneuver, controlthe autonomous vehicle 105 to complete the first MRC maneuver, andcontact the oversight system 350.

Service Station Detection

It is desirable for the in-vehicle control computer 150 to be able todetect service stations or terminals. For example, in the event that theautonomous vehicle 150 is still drivable but has one or more equipmentfailures that can be addressed by a service station or a terminal, thein-vehicle control computer 150 can be configured to route theautonomous vehicle 150 to a service station or a terminal to obtainrepairs or services.

As used herein, a service station may generally refer to a facility thatcan provide qualified service to the autonomous vehicle 150. Qualifiedservice can include qualified personnel as well as qualified equipmentto properly diagnose, and if advisable, fix any failures of theautonomous vehicle 105.

The in-vehicle control computer 150 may store a map within the memory175 that includes a map of the roadways and nearby environment (see theMap Taxonomy section of this application). The map can includeinformation of known service stations available for the autonomousvehicle 105. The map can further include known services available ateach of the stored service stations. The autonomous vehicle 105 and/orthe oversight system 350 may periodically (continuously in certainembodiments) update service station locations and available servicesinformation stored in the map.

The in-vehicle control computer 150 can be configured to evaluate thetype of a detected failure in order to determine the failure severitylevel (e.g., the level of criticality). In the event of a malfunction orfailure, the in-vehicle control computer 150 can request the oversightsystem 350 to run diagnostic checks to determine the failure severitylevel. In response, the in-vehicle control computer 150 can trigger anappropriate MRC maneuver or plan a visit to the nearest service stationbased on the failure severity level.

In response to determining that the failure severity level is less thana predetermined level (e.g., non-critical) and the closest servicestation is within a predetermined distance, the in-vehicle controlcomputer 150 can be configured to plan a new route to visit the closestservice station. For example, the in-vehicle control computer 150 can beconfigured to determine that a failure is a non-critical failure whenthe check engine tell-tale is on. As another example, the in-vehiclecontrol computer 150 can be configured to determine that a failure is acritical fault when the check engine tell-tale is blinking. Someexamples of non-safety critical failures include: a tire is starting todeflate, and the tire is not part of the steering system, a low coolantalarm, and oil pressure too high/too low.

In response to determining that the failure severity level is less thana predetermined level (e.g., non-critical) and the closest servicestation is farther than the predetermined distance, the in-vehiclecontrol computer 150 can be configured to plan to park itself on theshoulder (e.g., a safe area) or in an available truck parking lot andnotify the oversight system 350.

In response to determining that a failure severity level is greater thana predetermined level (e.g., a critical failure), the in-vehicle controlcomputer 150 can be configured to report to the oversight system 350that a visit to a service station is required.

The in-vehicle control computer 150 can be configured to detect aservice station from relevant traffic signs at a predetermined minimumdistance from the service station facility. The predetermined minimumdistance can include 200 m, 222 m, 250 m, 300 m, although other valuesare also possible. FIGS. 9J-9L illustrate example visualizations ofservice station signs.

The in-vehicle control computer 150 can also be configured to detect ifa service station is closed at a distance greater than a predeterminednumber of meters. The predetermined number of meters can include, forexample, 200 m, 222 m, 250 m, although other values are also possible.FIGS. 9M-9N illustrate example visualizations of service station signsindicating that a service station is either open or closed.

The in-vehicle control computer 150 can further be configured to followthe instructions given by traffic signs within the service stationfacility. The in-vehicle control computer 150 can also be configured todetect and follow instructions of traffic signs in the service station.FIG. 90 illustrates an example visualization of a service station thatcan include one or more signs having instructions. FIGS. 9P-9Rillustrate example visualizations of signs that can be located in aservice station.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect obstacles like cone markers, pedestrian, toolboxes,etc. in the autonomous vehicle's 105 trajectory while navigating througha service station. The in-vehicle control computer 150 can further beconfigured to wait for the autonomous vehicle's 105 turn and allow othervehicles in front to complete their service at designated parking areas.FIG. 9S illustrates an example visualization of one or more queues at aservice station.

The in-vehicle control computer 150 can be configured to detect parkinglines at a service station and park the autonomous vehicle 105 withinthe parking lines. The in-vehicle control computer 150 can also beconfigured to report to the oversight system 350 after the autonomousvehicle 105 is parked at the service station and then turn theautonomous vehicle's 105 engine off.

The in-vehicle control computer 150 can be configured to detect trafficconditions and move the autonomous vehicle 105 forward towards theservice station keeping a predetermined distance range of followingdistance from the lead vehicle. The predetermined distance range caninclude, for example, at least 2 m and no more than 4 m, at least 1.5 mand no more than 4.5 m, at least 1 m and no more than 5 m, althoughother ranges are also possible.

In some embodiments, the in-vehicle control computer 150 can also beconfigured to ensure safe departure of the autonomous vehicle 105 fromthe service station booth by detecting that there are no pedestrianapproaching or obstacles present in the departure trajectory.

The in-vehicle control computer 150 can further be configured to detectif a yield sign is present for vehicles leaving the service stationbooth and yield for vehicles that are merging with the autonomousvehicle's 105 trajectory path.

Low Vertical Clearance

Another type of physical infrastructure that the autonomous vehicle 105can be configured to detect includes low vertical clearanceobjects/areas. Low vertical clearance areas may not have sufficientclearance for the autonomous vehicle 105 to proceed along its route, andthus, it is important for the autonomous vehicle 105 to detect suchareas to avoid hitting any low clearance objects.

As used herein, vertical clearance may generally refer to a minimumdistance between the drivable pavement of the road and the lowest pointof any object located above the drivable pavement. FIG. 9T illustratesan example visualization of a bridge which has a vertical clearance. Inaddition, low vertical clearance may generally refer to a verticaldistance that is lower than the maximum height of the trailer or truckheight added to a predetermined number of meters. The predeterminednumber of meters can include, for example, 0.3 m, 0.5 m, 0.75 m,although other values are also possible.

The in-vehicle control computer 150 may store a map within the memory175 that includes a map of the roadways and nearby environment (see theMap Taxonomy section of this application). The map can includeinformation regarding signs that indicate bridges and other structuresalong with their corresponding vertical clearances. FIGS. 9U-9Xillustrate example visualizations of signs that may indicate thepresence of a low clearance area or object ahead. In some embodiments,the map can include the location, type, and/or vertical clearance of anyobject above the drivable pavement. The map can be periodically (e.g.,continuously in certain embodiments) updated if there is a change ofvertical clearance of any object, for example, dur to the presence of aconstruction zone.

The in-vehicle control computer 150 can be configured to detect verticalclearance information from relevant traffic signs at a predetermineddistance. The predetermined distance can include, for example, 222 m,although other distances are also possible without departing from thisdisclosure.

The in-vehicle control computer 150 can also be configured to monitorthe vertical clearance of any object with a distance greater than thedistance necessary to stop the vehicle with a deceleration of less thana predetermined deceleration. The predetermined deceleration caninclude, for example, 2.5 m/s², although other values are also possible.

In some embodiments, the in-vehicle control computer 150 can beconfigured to plan a route such that all vertical clearances are higherthan the low vertical clearance threshold value. The in-vehicle controlcomputer 150 can use the vertical clearance information from the map inplanning the route. The in-vehicle control computer 150 can also beconfigured to plan a new route in the case that there is any verticalclearance lower than vertical clearance threshold value, for example,detected when the autonomous vehicle 105 approaches the low verticalclearance object.

In response to detecting a low vertical clearance on the current path,the in-vehicle control computer 150 can be configured to slow theautonomous vehicle 105 down by a margin from the initial speed to give amore accurate measurement of the vertical clearance. The margin caninclude, for example a range from 5 mph to 15 mph less than the initialspeed, although other values are also possible.

The in-vehicle control computer 150 can be configured to inform theoversight system 350 of a detected vertical clearance location alongwith the corresponding height in response to detecting a verticalclearance on the current path of the autonomous vehicle 105. Thein-vehicle control computer 150 can be configured to stop the autonomousvehicle 105 before arriving at the low vertical clearance location if nonew route is possible with a clearance greater than the low verticalclearance threshold value. The in-vehicle control computer 150 can beconfigured to inform the oversight system 350 in the case that theautonomous vehicle 105 is stopped because of low vertical clearance.

Weigh Station

Another type of physical infrastructure that the in-vehicle controlcomputer 150 can be configured to detect is a weigh station. Certainlaws may request the autonomous vehicle 105 be weighed at weigh stationsat certain times. Thus, it is desirable that the in-vehicle controlcomputer 150 can detect and navigation weigh stations to comply withapplicable laws.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect weigh station vertical signals at a distancegreater than a predetermined distance to recognize the type of the weighstation. The predetermined distance can include, for example, 75 m, 100m, 125 m, 150 m, although other distances are also possible. In someimplementations, the weigh station type can include either classic orautomatic. The distance between a weigh station signal and the weighstation exit may be greater than 800 ft and less than one mile. FIGS.9Y-9AB illustrate example visualizations of signs that may indicate thepresence of a weigh station ahead.

The in-vehicle control computer 150 can be configured to detect a closedweigh station at a distance greater than a predetermined distance. Thepredetermined distance can include, for example, 75 m, 100 m, 125 m,although other distances are also possible. In some cases, a weighstation may be open when a screen with the word “closed” or the text“not open” is shown. In some embodiments, out on the highway bypassprogram transponders and mobile apps can have the ability to relay thesafety record and credentials of the autonomous vehicle 105 and itsfleet to the weigh station. When the autonomous vehicle 105 pulls into aweigh station, cameras can read the license plate number of theautonomous vehicle 105. FIGS. 9AC-9AE illustrate example visualizationsof signs and signals that may indicate whether a weigh station is openor closed.

When approaching an automatic weigh station, the in-vehicle controlcomputer 150 can be configured to detect the state of the weigh stationstatus signal at a distance greater than a predetermined distance. Thepredetermined distance can include, for example, 75 m, 100 m, 125 m, 150m, although other distances are also possible. The state can include anindication of the validity of the weigh station. In some embodiments,validity generally refer to whether the traffic signal is green (e.g.,valid) or red (e.g., invalid). A validation signal may generally referto a traffic signal with green and red lights.

When approaching an automatic weigh station, in response to detectingthat the validation signal is invalid and weigh station is closed, thein-vehicle control computer 150 can be configured to continue on itsroute without stopping at the weigh station.

In some embodiments, when driving on a highway, the in-vehicle controlcomputer 150 can be configured to detect the flashing light on top of avertical weigh station signal at a distance greater than a predetermineddistance to plan to enter weight station. The predetermined distance caninclude, for example, 75 m, 100 m, 125 m, 150 m, although otherdistances are also possible. One convention/rule on weigh station isthat a truck may exit towards a weigh station when a flashing light ison. There are other US states where the rule is that “All trucks mustenter the scales when NOT flashing”. The in-vehicle control computer 150can be configured to use the detection of the flashing light as adecision point to enter the weigh station.

In response to detecting the flashing light of a weight station isdetected and determining that the flashing signal is on, the in-vehiclecontrol computer 150 can be configured to enter the weight station.

When at an automatic weigh station and approaching a weigh-in-motionscale, the in-vehicle control computer 150 can be configured to slow theautonomous vehicle 105 down to a predetermined speed range. Thepredetermined speed range can include, for example, between 12 mph to 60mph, although other ranges are also possible.

When at a weigh station, the in-vehicle control computer 150 can beconfigured to bypass a weight check if the check is valid as indicatedby flashing green light signal or display with status check.

The in-vehicle control computer 150 can be configured to comply with adimensions check. For example, no out-of-size cargo may be allowed forsome pit stops/gates. Weigh station includes: real-time verification ofvehicle dimensions—integrate additional sensors (e.g., gantry-mountedlaser over height detectors) to determine if a commercial vehicleexceeds legal height, width, and length regulations and therefore wouldrequire an oversize/overweight permit.

When at the automatic weigh station and in case of any malfunction withthe autonomous vehicle 105 or the weigh check validation signal is notdeterministic, the in-vehicle control computer 150 can be configured toinform the oversight system 350 and wait at the weigh station for nextinstruction or the remote operator to take over control of theautonomous vehicle 105.

The in-vehicle control computer 150 can be configured to control theautonomous vehicle 105 to comply with the traffic rules as indicated bytraffic signs in weigh station area. The in-vehicle control computer 150can be configured to control the autonomous vehicle 105 to comply withthe safety-margins. As used herein, safety-margins may generally referto a distance from street lines, cars, pedestrians, or any object on theroad.

Toll Booth

Yet another type of physical infrastructure that can be detected by thein-vehicle control computer 150 is a toll booth. In some embodiments,the in-vehicle control computer 150 can be configured to identify andautonomously navigate toll booths. As used herein, a toll booth facilityor toll booth plaza may generally refer to an area in which the tollbooths are located to collect a toll (manually or electronically) fromvehicles in transit.

In some embodiments, the in-vehicle control computer 150 can beconfigured to identify the start or entrance point of a toll boothfacility based on detecting where the normal highway lanes start tospread out into multiple toll lanes or the distance to the toll booth isindicated by a traffic sign. The in-vehicle control computer 150 can beconfigured to identify the end point of a toll booth facility where themultiple toll lanes start to merge into the normal highway lanes. Insome cases, toll booth facilities may not spread into multiple lanes. Insuch cases, the in-vehicle control computer 150 can be configured toidentify the starting and end points of the toll booth facilities fromthe traffic signs that indicate the toll booth speed limit at thebeginning of the approach zone and the new speed limit at the end of thedeparture zone. FIGS. 9AF-9AI illustrate example visualizations of tollbooth facilities.

The in-vehicle control computer 150 can be configured to detect trafficsigns indicating when a toll booth is closed. FIGS. 9AJ-9AL illustrateexample visualizations of toll booth facilities including traffic signsindicating whether the corresponding toll booths are open or closed.

The in-vehicle control computer 150 can also be configured to detect atoll booth traffic sign and begin slowing down at a predeterminedminimum distance before reaching the toll booth facility. Thepredetermined minimum distance can include, for example, 525 m, 555 m,575 m, 600 m, although other values are also possible. In someembodiments, the in-vehicle control computer 150 can be configured touse maps and traffic signs to identify all the toll booths within theautonomous vehicle's 105 route. FIGS. 9AM-9AP illustrate examplevisualizations of a map and signs that indicate the presence of tollbooths.

The in-vehicle control computer 150 may store a map within the memory175 that includes a map of the roadways and nearby environment (see theMap Taxonomy section of this application). The map can includeinformation including traffic sign locations for toll booth facilitieswithin the path of the autonomous vehicle 105. The in-vehicle controlcomputer 150 can be configured to use the map and traffic signs toidentify the toll booths within the autonomous vehicle's 105 route.

The in-vehicle control computer 150 can further be configured to reducethe speed of the autonomous vehicle 105 gradually according to the tollbooth's speed limit. The in-vehicle control computer 150 can also beconfigured to use the autonomous vehicle's 105 engine break as firstoption to slow down.

In some embodiments, the in-vehicle control computer 150 can beconfigured to select a toll lane according to the autonomous vehicle's105 toll payment type at least a predetermined distance from the tollbooth. The predetermined distance can include 50 m, although otherdistances are also possible. An example of automatic payment type istoll pass electronic toll collection (ETC). The in-vehicle controlcomputer 150 can be configured to limit lateral dynamics of theautonomous vehicle 105 based on lateral maneuvers (e.g., lane change,lane bias, curbs) depending on the inertia of the trailer and stabilitycriteria. FIGS. 9AQ-9AR illustrate example visualizations of signsindicating the toll payment types accepted by a corresponding toll lane.

The in-vehicle control computer 150 can be configured to detectobstacles in the autonomous vehicle's 105 trajectory towards the tollbooth. The in-vehicle control computer 150 can be configured to wait forother vehicles to leave the toll booth before the autonomous vehicle 105enters the toll booth.

The in-vehicle control computer 150 can be configured to keep afollowing distance for the autonomous vehicle 105. For example, thein-vehicle control computer 150 can be configured to make a predictionon the expected target lane front vehicle deceleration, if any. Thein-vehicle control computer 150 can be configured to determine acritical distance with the target front vehicle as the largest gap fromthe following options: 1) the bumper-to-bumper gap required to maintaina high confidence in our sensor coverage, 2) the bumper-to-bumper gaprequired to be outside of our response time minimums, and 3) thebumper-to-bumper gap required to avoid a collision under the assumptionthat both the autonomous vehicle 105 and the target lane front vehiclehave to decelerate to a complete stop at the expected deceleration ofthe target lane front vehicle and the autonomous vehicle's 105 expectedreactive deceleration. This gap may account for the autonomous vehicle's105 reaction time and may include an additional safety buffer. In someembodiments, if the target front vehicle is not expected to decelerate,this gap may be equal to the safety buffer. The in-vehicle controlcomputer 150 can be configured to maintain a predetermined minimumdistance in this situation.

In some embodiments, the in-vehicle control computer 150 can beconfigured to avoid a lane change within the critical distance to thetarget lane front vehicle. For all but critical safety lane changeintentions, the in-vehicle control computer 150 can be configured toprefer to change lanes with a bumper-to-bumper gap of at least apredetermined distance with the target front vehicle, follow adeceleration behavior, and avoid lane changes behind a target frontslow-moving vehicle. The predetermined distance can include, forexample, 10 meters, 12.5 meters, 15 meters, 17.5 meters, 20 meters,although other values are also possible. FIG. 9AS illustrates an examplevisualization of a front distance between the autonomous vehicle and atarget lane front vehicle.

In response to determining that a toll gate is closed, the in-vehiclecontrol computer 150 can be configured to control the autonomous vehicle105 to a full stop at a predetermined distance from the toll gate. Thepredetermined distance can include, for example, 1 m, 2 m, 3 m, althoughother values are also possible.

The in-vehicle control computer 150 can be configured to increase thespeed of the autonomous vehicle 105 after passing the toll gate with apredetermined acceleration until the allowed speed limit is reached. Thepredetermined acceleration can include, for example, 1 m/s2, 1.5 m/s2, 2m/s2, although other values are also possible.

The in-vehicle control computer 150 can be configured to confirm thetoll payment has been registered and report any issue with the paymentimmediately to the oversight system 350. The in-vehicle control computer150 can be configured to identify the lane indicated by the map tocontinue the trip.

FIG. 9AT illustrates an example visualization of a toll booth facility.It may be common to find that toll lanes are altered 902, then aftertoll gate 904 the lanes are restored and start again 906. Also, it maybe common to find some toll lanes are out of service, shut down formaintenance and construction or due to accidents, etc.

The in-vehicle control computer 150 can be configured to ensure a safedeparture from the toll booth by ensuring there is no otherentities/objects like a toll gate or vehicles are blocking theautonomous vehicle's 105 path.

In response to determining that a lane change is required whilenavigating the toll booth facilities, the in-vehicle control computer150 can be configured to control the autonomous vehicle 105 to perform alane change.

The in-vehicle control computer 150 can be configured to detect andavoid collisions between the autonomous vehicle 105 and any pedestrians,obstacles, and vehicles, within the toll booth facilities, by keeping apredetermined safe distance from these items. The predetermined safedistance can include, for example, 1 m, 2 m, 3 m, although other valuesare also possible. For example, the autonomous vehicle 105 can avoidrunning over a pedestrian or toll booth attendant.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect if a yield sign is present or if yielding isrequired for other vehicles that are also leaving the toll booth. TheETC lanes with favorable geometry typically allow vehicles to movethrough the toll plaza without stopping, but usually within a setregulatory speed limit or advisory speed. The in-vehicle controlcomputer 150 can be configured to resume the trip for the autonomousvehicle 105 until the blocking obstacle, vehicle or pedestrian has beencleared from the autonomous vehicle's 105 trajectory.

Ramp Meter

Still yet another type of physical infrastructure that the in-vehiclecontrol computer 150 can detect are ramp meters. As used herein, rampmeters may generally refer to traffic signals installed on freewayon-ramps to control the frequency at which vehicles enter the flow oftraffic on the freeway. There may be at least two kind of ramp meters:pre-timed controlled and traffic controlled ramp meters.

As used herein, pre-timed controlled may generally refer to time-of-dayor fixed time ramp meters. In such case, ramp meters can be activatedbased on pre-set schedules. Such meters are either showing a steadygreen light or are turned off.

As used herein, traffic controlled ramp meters may establish meteringrates based on actual freeway conditions. FIG. 9AU illustrates anexample visualization of a ramp with a ramp meter.

The in-vehicle control computer 150 may store a map within the memory175 that includes a map of the roadways and nearby environment (see theMap Taxonomy section of this application). The map can includeinformation regarding ramp meters' location and traffic signs related toramp meters. FIGS. 9AV-9AZ illustrate example visualizations of signsthat can indicate the presence of a ramp meter.

The map can further include information regarding a ramp meter's flowcontrol scheme. The flow control scheme can define the number ofvehicles allowed to drive on the ramp when the green light is on. Forexample, one vehicle may be allowed per green, two vehicles may beallowed per green, one vehicle may be allowed per green each lane. FIGS.9BA-9BC illustrate example visualizations of signs that can indicateflow control schemes.

The in-vehicle control computer 150 can be configured to detect rampmeter ahead signs and whether there is a flashing light to warn that theramp meter is in operation. When a flashing light is present, the lightblinking may indicate that the ramp meter is in operation and the lightbeing off may indicate that the ramp meter is not in operation. FIGS.9BD-9BE illustrate example visualizations of signs and/or flashinglights that can indicate the presence of a ramp meter.

The in-vehicle control computer 150 can also be configured to detectramp meter traffic lights and the flow control scheme signs. FIGS.9BF-9B1 illustrate example visualizations of signs and/or flashinglights that can indicate the presence of ramp meter traffic lights andflow control schemes.

In some embodiments, the in-vehicle control computer 150 can beconfigured to control the autonomous vehicle 105 to slow down at apredetermined distance from the ramp lights, travel at a speed of apredetermined maximum velocity, and maintain the minimum followingdistance if there is a vehicle in front of the autonomous vehicle 105.The predetermined distance can include, for example, 200 m, 225 m, 250m, 275 m and the predetermined maximum velocity can include, forexample, 15 MPH, 20 MPH, 25 MPH, although other values are also possiblewithout departing from aspects of this disclosure.

In response to detecting that the ramp meter light is red, thein-vehicle control computer 150 can be configured to stop the autonomousvehicle 105 no further than a predetermined first distance from thelight line or no further than a predetermined second distance behind afront vehicle. The predetermined first distance can include, forexample, 3 meters, 4 meters, 5 meters and the predetermined seconddistance can include, for example, 1 meter, 2 meters, 3 meters, althoughother distances are also possible. FIG. 9BJ illustrates an examplevisualization of traffic stopped at a ramp meter.

The in-vehicle control computer 150 can be configured to control theautonomous vehicle 105 to proceed through the ramp beyond the light linein response to determining that the light is green and the autonomousvehicle 105 is included in the number of allowed vehicles given by theflow control scheme in the autonomous vehicle's 105 lane. For example,if the flow control scheme mentions 1 vehicle per green, the autonomousvehicle 105 is allowed to proceed through the ramp only if it was thefirst vehicle at the ramp light signal.

In response to detecting another entity (e.g., a vehicle) whileapproaching the ramp meter lights, the in-vehicle control computer 150can further be configured to plan to control the autonomous vehicle 105to come to a complete stop a predetermined distance away from theentity. The predetermined distance can include, for example, 3 m, 4 m, 5m, although other values are also possible.

In response to detecting a crosswalk, the in-vehicle control computer150 can be configured to control the autonomous vehicle 105 to stopwhere the autonomous vehicle's 105 front most point is no further than apredetermined distance from the crosswalk line. The predetermineddistance can include, for example, 3 m, 4 m, 5 m, although otherdistances are also possible.

After crossing the ramp meter lights and entering into the ramp whichleads to the roadway, the in-vehicle control computer 150 can beconfigured to adjust the speed of the autonomous vehicle 105 in the rampmeter lane so that the autonomous vehicle 105 can find sufficientlateral width free to merge into a freeway lane. Getting the lateralsides of the autonomous vehicle 105 free permits the autonomous vehicle105 to find a gap for merging in the requested lane and yielding toother vehicles whose bumper is ahead of the autonomous vehicle 105.

The in-vehicle control computer 150 can be configured to follow trafficusing a zipper merge method in case of multiple lanes of the ramp mergeinto freeway lanes. After passing the ramp meter lights, the in-vehiclecontrol computer 150 can be configured to yield to vehicles in otherlanes, if the front most point of that vehicle is ahead of theautonomous vehicle 105. After that, the in-vehicle control computer 150can be configured to control the autonomous vehicle 105 to move forwardto merge into the lane.

When the autonomous vehicle 105 is driving on a freeway and thein-vehicle control computer 150 detects roadway vehicles merging in theautonomous vehicle's 105 lane, the in-vehicle control computer 150 canbe configured to control the autonomous vehicle 105 to follow mergerules. FIG. 9BK illustrates an example visualization of merging traffic.

Bumps

Another type of physical infrastructure that the in-vehicle controlcomputer 150 can be configured to detect is bumps. As described herein,the in-vehicle control computer 150 may store a map within the memory175 that includes a map of the roadways and nearby environment (see theMap Taxonomy section of this application). The map can includeinformation regarding speed bumps, their type (e.g., speed hump, speedcushion, or speed table), and the distance between each bump. FIG. 9BLillustrates an example visualization of a speed bump.

As used herein, a speed bump may generally refer to a traffic calmingdevice that uses vertical deflection to slow motor-vehicle traffic inorder to improve safety conditions. Speed bumps can include, forexample, speed humps, speed cushions, and speed tables.

As used herein, a speed hump may generally refer to a device whichcreates a gentle rocking sensation in a vehicle passing over the speedhump at the posted speed limit. If a vehicle is driving at unsafe speed,the hump will jar the vehicle and its contents, causing discomfort tothe occupants and disruption to cargo. FIGS. 9BM-9BO illustrate examplevisualizations of speed humps.

As used herein, a speed cushion may generally refer to a speed hump thatincludes wheel cutouts to allow large vehicles to pass unaffected, whilereducing passenger vehicle speeds. They can be offset to allow unimpededpassage by emergency vehicles and are typically used on key emergencyresponse routes. FIG. 9BP illustrates an example visualization of aspeed cushion.

As used herein, a speed table may generally refer to a very long andbroad speed hump, or for a flat-topped speed hump, where sometimes apedestrian crossing is provided in the flat portion of the speed table.FIG. 9BQ illustrates an example visualization of a speed table.

The in-vehicle control computer 150 can be configured to detect trafficsigns or pavement markings indicating the presence of speed bumps at apredetermined minimum distance. The predetermined minimum distance caninclude, for example, 200 m, 222 m, 250 m, 275 m, although other valuesare also possible.

The in-vehicle control computer 150 can be configured to detect if aspeed bump is a temporary or permanent speed bump. For example, thein-vehicle control computer 150 can be configured to consider a portablebump as a temporary speed bump and an asphalt bump as a permanent speedbump. FIG. 9BR illustrates an example visualization of a portable speedbump.

The in-vehicle control computer 150 can be configured to determine ifthere is a conflict between the mapped and perceived speed bumpinformation. In response, the in-vehicle control computer 150 can beconfigured to inform the oversight system 350 and prioritize theperceived information.

The in-vehicle control computer 150 can also be configured to inform theoversight system 350 in case of a not mapped permanent speed bump or incase of non-detected mapped permanent speed bump. FIG. 9BS illustratesan example visualization of a sign indicating the presence of a speedhump.

The in-vehicle control computer 150 can further be configured to detecttraffic signs indicating the presence of speed bumps and determine thetype of speed bump as the autonomous vehicle 105 approaches the start ofthe speed bump.

In some embodiments, the in-vehicle control computer 150 can beconfigured to approach a detected speed bump with a predeterminedmaximum speed and start accelerating to the level of allowed speed limitwhen rear most point of the autonomous vehicle 105 crosses the lastspeed bump. The predetermined maximum speed can include, for example, 4mph, 5 mph, 6 mph, 8 mph, although other values are also possible.

The in-vehicle control computer 150 can also be configured to detect aspeed limit traffic sign after the autonomous vehicle 105 crosses aspeed bump to increase the speed as appropriate. FIG. 9BT illustrates anexample visualization of a speed bump and a sign indicating a speedlimit for the speed bump.

The in-vehicle control computer 150 can also be configured to detectwhether a pedestrian is crossing the road using a cross walk areabetween ends of a speed table when the autonomous vehicle 105 isapproaching the speed table at a predetermined minimum distance. Thepredetermined minimum distance can include, for example, 200 m, 222 m,250 m, 275 m, although other distances are also possible.

The in-vehicle control computer 150 can also be configured to controlthe autonomous vehicle 105 to cross a speed table bump at a maximumspeed and stop the autonomous vehicle 105 in response to detecting apedestrian. The maximum speed can include, for example, 5 mph, althoughother speeds are also possible. FIG. 9BU illustrates an examplevisualization of a speed table with a cross walk.

As used herein, a pothole may generally refer to a depression in a roadsurface, usually asphalt pavement, where traffic has removed brokenpieces of the pavement. Pothole may usually result from water in theunderlying soil structure and traffic passing over the affected area.FIG. 9BV illustrates an example visualization of a pothole.

The in-vehicle control computer 150 can also be configured to detect thepresence of a pothole and detect the location of the pothole (e.g.,including the distance from the autonomous vehicle 105 to the pothole),as well as the width and depth of the pothole at a predetermined minimumdistance. The predetermined minimum distance can include, for example,200 m, 222 m, 250 m, 275 m, although other values are also possible.

In some embodiments, the in-vehicle control computer 150 can beconfigured to determine that a pothole is an avoidable pothole inresponse to the detected pothole having a width less than that of theautonomous vehicle's 105 tire width and a depth less than apredetermined percentage of the autonomous vehicle's 105 wheel diameter.The predetermined percentage can include, for example, 4%, 5%, 6%,although other values are also possible.

The in-vehicle control computer 150 can be configured to determine thata pothole is an avoidable pothole when the autonomous vehicle 105 can goaround the pothole using lane bias.

The in-vehicle control computer 150 can also be configured to determinethat a pothole is an unavoidable pothole in response to the potholecovering more than a predetermined amount of the lateral width of thelane and lane bias cannot be used to avoid the pothole. Thepredetermined amount can include, for example, 25%, 30%, 35%, althoughother values are also possible.

In response to detecting an unavoidable pothole having a width less thanthe autonomous vehicle's 105 tire width and a depth less than thethreshold percentage of the autonomous vehicle's 105 wheel diameter, thein-vehicle control computer 150 can be configured to slow the autonomousvehicle 105 down to a predetermined amount of the current speed limitand plan to go over them. The predetermined amount can include, forexample, 8%, 10%, 12%, although other values are also possible.

In response to detecting an avoidable pothole, the in-vehicle controlcomputer 150 can be configured to slow the autonomous vehicle 105 downto a predetermined threshold percentage of the current speed limit andperform lane bias. The predetermined amount can include, for example,8%, 10%, 12%, although other values are also possible.

In response to detecting a pothole in lane which is bigger than theautonomous vehicle's 105 tire width and has a depth greater than athreshold percentage (e.g., 8%, 10%, 12%) of the autonomous vehicle's105 wheel diameter that can potentially cause damage, the in-vehiclecontrol computer 150 can be configured to perform a critical lane changeto ensures that the autonomous vehicle's 105 wheels do not hit thedetected pothole. The predetermined percentage can include, for example,8%, 10%, 12%, although other values are also possible. In the case thatthe in-vehicle control computer 150 determines that a lane change is notpossible, the in-vehicle control computer 150 can be configured to planto stop the autonomous vehicle 105 a predetermined distance ahead of thepotentially damaging pothole and inform the oversight system 350. Thepredetermined distance can include, for example, 4 meters, althoughother values are also possible.

Emergency Lanes

Still another type of physical infrastructure that the in-vehiclecontrol computer 150 can be configured to detect is an emergency lane.Emergency lanes may be useful when the autonomous vehicle 105 needs toexecute a first MRC maneuver. Emergency lanes may be a type of safe zonein which the autonomous vehicle 105 can complete an MRC maneuver.

The in-vehicle control computer 150 can be configured to identifysituations related to the first MRC maneuver. As described herein, thefirst MRC maneuver can involve pulling the autonomous vehicle 105 onto ashoulder to safely stop.

As used herein, a road shoulder may generally refer to a strip of landimmediately adjacent to the traffic lane of a road not bordered by curband channel. FIG. 9BW illustrates an example visualization of a roadwaywith shoulders.

As used herein, a highway shoulder may generally refer to a right mostor left most lane of a highway that is not operational or withouttraffic.

When travelling on a highway, the in-vehicle control computer 150 can beconfigured to determine that a shoulder is a permanent shoulder or apart time shoulder (outside of the shoulder's access hours) having awidth that is more than a predetermined number of times of theautonomous vehicle's 105 width as an available shoulder for stop. Thepredetermined number of times can include, for example, 1.1 times, 1.2times, 1.25 times, 1.3 times, although other values are also possible.FIG. 9BX illustrates an example visualization of a highway with ashoulder.

When travelling on a local road, the in-vehicle control computer 150 canbe configured to consider a shoulder having width greater than apredetermined number of times of the autonomous vehicle's 105 width asan available shoulder. The predetermined number of times can include,for example, 1.1 times, 1.2 times, 1.25 times, 1.3 times, although othervalues are also possible.

The in-vehicle control computer 150 may store a map within the memory175 that includes a map of the roadways and nearby environment (see theMap Taxonomy section of this application). The map can includeinformation regarding the nearest available shoulder for stopping theautonomous vehicle 105.

The in-vehicle control computer 150 can be configured to detect thenearest available shoulder for stopping at a predetermined distance. Thepredetermined distance can include, for example, 200 m, 222 m, 250 m,275 m, although other values are also possible.

The in-vehicle control computer 150 can be configured to estimate theentry point where the autonomous vehicle 105 can start entering into theshoulder.

In some embodiments, the in-vehicle control computer 150 can beconfigured to estimate the speed at which the autonomous vehicle 105 mayenter into the shoulder and plan to stop the autonomous vehicle 105 whenthe lane change is complete.

The in-vehicle control computer 150 can be configured to detect anyobstacle like barricades, marker cone, or barriers in the availableshoulder at a predetermined distance. The predetermined distance caninclude, for example, 200 m, 222 m, 250 m, 275 m, although other valuesare also possible.

The in-vehicle control computer 150 can be configured to estimate theremaining length of the available shoulder in case any barricade orvehicle is present in the shoulder before the autonomous vehicle 105enters into the shoulder to pull over.

The in-vehicle control computer 150 can be configured to detect theshoulder composition (e.g., asphalt, concrete, mixed, grating, scrapedroad, potholes) at a predetermined distance. The predetermined distancecan include, for example, 200 m, 222 m, 250 m, 275 m, although othervalues are also possible.

In some embodiments, the in-vehicle control computer 150 can beconfigured to estimate the coefficient of friction of the shoulder basedon the detected shoulder composition. The in-vehicle control computer150 can also be configured to estimate the braking distance required onthe upcoming available shoulder considering the target speed at whichthe autonomous vehicle 105 will approach the shoulder. In someembodiments, the in-vehicle control computer 150 can be configured toenter the shoulder in response to determining that the available lengthof the shoulder is greater than a braking distance of the autonomousvehicle 105 plus a length of the autonomous vehicle 105 plus apredetermined margin distance. The predetermined margin distance caninclude, for example, 1.5 m, 2 m, 2.5 m, 3 m, although other values arealso possible.

Roadway Width

Still yet another type of physical infrastructure that the in-vehiclecontrol computer 150 can be configured to detect is the width of theroadway. As used herein, the road width may generally refer to thedimension of the roadway width up to a predetermined distance. Thepredetermined distance can include, for example, 40 m, 45 m, 48.96 m, 50m, 55 m, although other values are also possible. There may be four mainroad types that the autonomous vehicle 105 may encounter: state highway(two-lane road for each direction); national highway; district road;rural roads or village roads. FIGS. 9BY-9CA illustrate examplevisualizations of roadways having different widths.

As used herein, a road segment may generally refer to a piece of theroadway. For example, road segments can include parts of curved roadsegments, superelevation road segments, or straight roads.

As used herein, a road lane may generally refer to a road segment wherethe size of the road lane multiplied by the total number of lanes givesthe road width.

The in-vehicle control computer 150 may store a map within the memory175 that includes a map of the roadways and nearby environment (see theMap Taxonomy section of this application). The map can includeinformation including metadata regarding road segments with the roadlane width. The lane width may be defined such that it does not changeon the same road. The map can also include metadata on road segmentswith the number of lanes.

The in-vehicle control computer 150 can be configured to detect roadwayswith different width, and in response, the in-vehicle control computer150 can be configured to adapt the motion planning of the autonomousvehicle 105 with help of the onboard perception system (e.g., thevehicle sensor subsystems 144) to drive on an estimated right lane ofthe road. Lane width may have an impact on motion planning since theautonomous vehicle 105 may have less margin for error on narrower lanes.

The in-vehicle control computer 150 can also be configured to plan analternate route in response to determining that the autonomous vehicle105 has come across a road with a width greater than a predetermineddistance. The predetermined distance can include, for example, 40 m, 45m, 48.96 m, 50 m, 55 m, although other distances are also possible.

The in-vehicle control computer 150 can be configured to position theautonomous vehicle 105 on the center of the lane (e.g., the road lanewidth divided by 2 from the outer edge of the road).

The in-vehicle control computer 150 can be configured to prefer drivingthe autonomous vehicle 105 in the right most lane for its mission unlessthe in-vehicle control computer 150 can be configured to determines thata lane change lane can be used for passing around slow moving orstationary vehicles/entities. In some situations, when the autonomousvehicle 105 is embodied as a truck, it may have to drive on themost-right lane due to speed limits (some lanes have a minimum speed)and overtake constraints. FIGS. 9CB-9CD illustrate examplevisualizations of signs that indicate whether trucks are permitted in agiven lane or roadway.

The in-vehicle control computer 150 can be configured to control theautonomous vehicle 105 to overtake a slow moving vehicle in front if ado not pass sign is not detected for the corresponding stretch of theroad and the left-hand side lane width allows the autonomous vehicle 105to perform the maneuver.

In some embodiments, the in-vehicle control computer 150 can beconfigured to prefer roads with lane markings. For example, there areroads where markings are not visible or eroded with weather. Thein-vehicle control computer 150 can be configured to plan to drive onroads with reliable markings, for example, as reflected in the map.

In response to determining that markings are not present or visible, thein-vehicle control computer 150 can be configured to derive the centralposition of the road and find or establish virtual markings. There areroads where markings are not visible or eroded with weather. Thein-vehicle control computer 150 can be configured to find the center ofthe road and derive the virtual lane marks.

In response to detecting extra wide lanes (e.g., lanes with a widthgreater than a predetermined width), the in-vehicle control computer 150can be configured to adapt the motion planning with help of the onboardperception system to estimate the center of such extra wide lane andcontinue the autonomous vehicle's 105 mission.

Tunnel Detection

Another type of physical infrastructure that the in-vehicle controlcomputer 150 can be configured to detect are tunnels. It can bedesirable to detect the presence of tunnels both to ensure that theautonomous vehicle 105 can safely traverse the vertical clearance of thetunnel as well as to plan for any loss of wireless data connection tothe oversight system 350 while inside the tunnel.

As used herein, vertical clearance may generally refer to the minimumdistance between the drivable pavement of the road and the lowest pointof any object located above the drivable pavement. FIG. 9CE illustratesan example visualization of the vertical clearance of a tunnel.

As used herein, low vertical clearance may generally refer to thevertical distance lower than the maximum height of the trailer or truckheight of the autonomous vehicle 105 with addition of a predetermineddistance. The predetermined distance can include, for example, 0.25 m,0.5 m, 0.75 m, although other values are also possible.

The in-vehicle control computer 150 may store a map within the memory175 that includes a map of the roadways and nearby environment (see theMap Taxonomy section of this application). The map can includeinformation regarding signs that indicate the presence of a tunnel aswell as the location of the tunnel. The map can be periodically (e.g.,continuously in some embodiments) updated to include closed tunnellocations. The map can also include the availability of GPS receptionand its function based on historical observation for tunnels in the map.

The in-vehicle control computer 150 can be configured to detect thepresence of a tunnel from relevant traffic at a predetermined distance.The predetermined distance can include, for example, 200 m, 222 m, 250m, although other distances are also possible. The in-vehicle controlcomputer 150 can be configured to determine stopping distance as thedistance necessary to stop with a predetermined deceleration. Thepredetermined deceleration can include, for example, 2.25 m/s2, 2.5m/s2, 2.75 m/s2, although other values are also possible.

The in-vehicle control computer 150 can be configured to detect if atunnel is closed from relevant traffic signs at a predetermineddistance. The predetermined distance can include, for example, 200 m,222 m, 250 m, although other values are also possible.

In some embodiments, the in-vehicle control computer 150 can beconfigured to detect the vertical clearance information for a tunnelfrom relevant traffic signs at a predetermined distance. Thepredetermined distance can include, for example, 200 m, 222 m, 250 m,although other values are also possible. FIGS. 9CF-9CG illustrateexample visualizations of a tunnel and signs that indicate the verticalclearance for the tunnel.

The in-vehicle control computer 150 can be configured to detect therequirement for usage of headlights from relevant illumination trafficsigns at a predetermined distance. The predetermined distance caninclude, for example, 200 m, 222 m, 250 m, although other values arealso possible. FIG. 9CH illustrates an example visualization of a signindicating that headlights should be used in the upcoming tunnel.

In some embodiments, the in-vehicle control computer 150 can beconfigured to periodically (or continuously in some embodiments) monitorthe vertical clearance on the planned path in front of the autonomousvehicle 105 with a distance greater than the distance necessary to stopthe vehicle with a predetermined deceleration. The predetermineddeceleration may include, for example, 2.25 m/s2, 2.5 m/s², 2.75 m/s2,although other values are also possible.

In response to determining that the autonomous vehicle 105 is inside atunnel, the in-vehicle control computer 150 can be configured todetermine if the autonomous vehicle's 105 GPS receiver's signal strengthindicator (RSSI) is less than a threshold level. The threshold level caninclude, for example, 25%, 30%, 35%, although other values are alsopossible.

In some embodiments, the in-vehicle control computer 150 can beconfigured to plan a route such that all vertical clearances are higherthan the low vertical clearance threshold value.

In response to detecting a tunnel on the autonomous vehicle's 105current path, the in-vehicle control computer 150 can be configured toslow the autonomous vehicle 105 down by a margin from the initial speedto give a more accurate measurement. The initial margin can include, forexample, between 5 to 10 mph, although other values are also possible.

When the autonomous vehicle 105 is in a tunnel, the in-vehicle controlcomputer 150 can be configured to avoid lane changes other than criticallane changes.

The in-vehicle control computer 150 can be configured to inform theoversight system 350 that the autonomous vehicle 105 will enter a tunnela predetermined length of time before entering the tunnel. Thepredetermined length of time can include, for example, 8 s, 10 s, 12 s,15 s, although other values are also possible. In particular, thein-vehicle control computer 150 can be configured to inform the yardoperator that the autonomous vehicle 105 will enter the tunnel. Thein-vehicle control computer 150 can also be configured to inform theoversight system 350 in response to the autonomous vehicle 105 exitingthe tunnel. The in-vehicle control computer 150 can also be configuredto inform the yard operator. If the oversight system 350 or the yardoperator does not receive confirmation that the autonomous vehicle 105has left the tunnel within a predetermined length of time, the oversightsystem 350 may send help to assist in case the autonomous vehicle 105was unable to complete navigation through the tunnel.

The in-vehicle control computer 150 can be configured to turn on theautonomous vehicle's 105 headlights in response to entering a tunnel.

In some embodiments, the in-vehicle control computer 150 can beconfigured to use an alternate form of localization if there is no GPSreception in the tunnel. For example, the in-vehicle control computer150 can be configured to use on board sensors (e.g., cameras, Radar, andLidar) for truck localization in the tunnel.

In response to determining that low vertical clearance is present on theautonomous vehicle's 105 path, the in-vehicle control computer 150 canbe configured to inform the oversight system 350 of the verticalclearance's location and height.

In response to detecting a low vertical clearance is detected anddetermining that no alternate route is available, the in-vehicle controlcomputer 150 can be configured to control the autonomous vehicle 105 tostop on a shoulder or emergency lane, or pull over to the rightmost laneno less than a predetermined distance away from the low verticalclearance and inform the oversight system 350. The predetermineddistance can include, for example, 40 m, 45 m, 50 m, 55 m, 60 m,although other values are also possible. The in-vehicle control computer150 can be configured to turn on the autonomous vehicle's 105 hazardlights and/or trigger the first MRC maneuver.

In response to detecting a closed tunnel, the in-vehicle controlcomputer 150 can be configured to control the autonomous vehicle 105 tostop on a shoulder or emergency lane, or pull over the rightmost lane,and inform the oversight system. The in-vehicle control computer 150 canbe configured to turn on the autonomous vehicle's hazard lights and/ortrigger the first MRC maneuver.

In response to determining that a tunnel is closed due to an emergencysituation and detecting the presence of emergency traffic controldevices, the in-vehicle control computer 150 can be configured to stopon a shoulder or emergency lane, or pull over to the rightmost lane noless than a predetermined distance away from traffic control devices,and inform the oversight system 350. The predetermined distance caninclude, for example, 40 m, 45 m, 50 m, 55 m, 60 m, although othervalues are also possible. The in-vehicle control computer 150 can beconfigured to turn on the autonomous vehicle's 105 hazard lights and/ortrigger the first MRC maneuver.

When the autonomous vehicle 105 has stopped because of low verticalclearance and if no alternative route is available, the in-vehiclecontrol computer 150 can be configured to inform the oversight system350.

Example Technique for Controlling an Autonomous Vehicle During an MRCManeuver

One objective of this disclosure includes controlling an autonomousvehicle 105 during an MRC maneuver. FIG. 9CI illustrates an examplemethod which can be used to control the autonomous vehicle 105 during anMRC maneuver. The method 910 may be described herein as being performedby one or more processors, which may include the in-vehicle controlcomputer 150.

The method 910 begins at block 911. At block 912, the in-vehicle controlcomputer 150 is configured to determine a minimal risk condition (MRC)maneuver for the autonomous vehicle to execute. The MRC maneuver mayinclude a first MRC maneuver in which the autonomous vehicle 105 pullsover to a safe zone on a shoulder of the roadway.

At block 914, the in-vehicle control computer 150 is configured toidentify a safe zone in which the autonomous vehicle 105 is able toexecute the MRC maneuver by coming to a stop based on perception datareceived from at least one perception sensor of the autonomous vehicle.The at least one perception sensor is configured to generate theperception data to be indicative of physical infrastructure (e.g.,including the safe zone) on or near a roadway.

At block 916, the in-vehicle control computer 150 is configured toidentify one or more exclusion zones within the safe zone based on theperception data. The exclusion zone may identify an area in which theautonomous vehicle 105 cannot enter during the MRC maneuver.

At block 918, the in-vehicle control computer 150 is configured tocontrol the autonomous vehicle to execute the MRC maneuver includingstopping outside of the exclusion zone. The method 910 ends at block920.

CONCLUSION

All of the above reactive features and supporting features may presentin an autonomous truck in a reasonable combination.

Though much of this document refers to an autonomous truck, it should beunderstood that any autonomous ground vehicle may have such features.Autonomous vehicles which traverse over the ground may include: semis,tractor-trailers, 18 wheelers, lorries, class 8 vehicles, passengervehicles, transport vans, cargo vans, recreational vehicles, golf carts,transport carts, and the like.

While several embodiments have been provided in this disclosure, itshould be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of this disclosure. The present examples are to be consideredas illustrative and not restrictive, and the intention is not to belimited to the details given herein. For example, the various elementsor components may be combined or integrated in another system or certainfeatures may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of this disclosure. Other itemsshown or discussed as coupled or directly coupled or communicating witheach other may be indirectly coupled or communicating through someinterface, device, or intermediate component whether electrically,mechanically, or otherwise. Other examples of changes, substitutions,and alterations are ascertainable by one skilled in the art and could bemade without departing from the spirit and scope disclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

What is claimed is:
 1. An autonomous vehicle comprising: at least oneperception sensor configured to generate perception data indicative ofconditions of the autonomous vehicle on a roadway; a processor; and anon-transitory computer readable medium having stored thereoninstructions that, when executed by the processor, cause the processorto: determine an accident in which the autonomous vehicle has beeninvolved based on the perception data, determine a severity of theaccident, determine a course of action to reduce further damage to theautonomous vehicle and any other entities on or near the roadway, andcause the autonomous vehicle to navigate based on the determined courseof action.
 2. The autonomous vehicle of claim 1, wherein the processoris further configured to: determine a deceleration of the autonomousvehicle; determine that the deceleration of the autonomous vehicle isgreater than a threshold deceleration; and determine that a severity ofthe accident is severe in response to determining that the decelerationof the autonomous vehicle is greater than the threshold deceleration. 3.The autonomous vehicle of claim 1, wherein the processor is furtherconfigured to: determine that the autonomous vehicle has collided withan object; determine that the object is: a pedestrian, cyclist,motorcycle, or other vulnerable road user (VRU); and determine that aseverity of the accident is severe in response to determining that theobject is: a pedestrian, cyclist, motorcycle, or other vulnerable roaduser (VRU).
 4. The autonomous vehicle of claim 3, wherein the course ofaction comprises controlling the autonomous vehicle to remain stationaryin response to determining that the severity of the accident is severe.5. The autonomous vehicle of claim 1, wherein the processor is furtherconfigured to: determine that the autonomous vehicle was moving at atime of the accident, wherein the course of action comprises controllingthe autonomous vehicle to make a complete stop in response determiningthat the autonomous vehicle was moving at the time of the accident. 6.The autonomous vehicle of claim 1, further comprising: a networkcommunications subsystem, wherein the processor is further configuredto: perform a diagnostic procedure to identify whether vehicle criticalsystems are still functioning in response to determining that theaccident has occurred, and provide results of the diagnostic procedureto an oversight system via the network communications subsystem.
 7. Theautonomous vehicle of claim 6, wherein the vehicle critical systemscomprise systems of the autonomous vehicle involved in performing aminimal risk condition (MRC) maneuver.
 8. The autonomous vehicle ofclaim 1, wherein the processor is further configured to: determine thata severity of the accident is light in response to determining that oneor more of the following conditions is satisfied: there is no bodydamage to the autonomous vehicle, the engine is still running, thediagnostic results do not show any malfunction of the autonomousvehicle, and/or there are no debris and/or obstacles which will restrictmovement of the autonomous vehicle, wherein the course of actioncomprises controlling the autonomous vehicle to execute a minimal riskcondition (MRC) maneuver in response to determining that the severity ofthe accident is light.
 9. The autonomous vehicle of claim 1, wherein theat least one perception sensor comprises at least one of: an inertialsensor, a camera, and/or a lidar.
 10. A non-transitory computer-readablemedium having stored thereon instructions which, when executed by aprocessor, cause the processor to: determine whether an autonomousvehicle has been involved in an accident based on perception datareceived from at least one perception sensor configured to generate theperception data indicative of conditions of the autonomous vehicle on aroadway; in response to determining that the autonomous vehicle has beeninvolved in an accident, determine a severity of the accident; determinea course of action to reduce further damage to the autonomous vehicleand any other entities on or near the roadway; and cause the autonomousvehicle to navigate based on the determined course of action.
 11. Thenon-transitory computer-readable medium of claim 10, wherein theinstructions further cause the processor to: cause the autonomousvehicle to perform an evasive maneuver before determining that theaccident has occurred.
 12. The non-transitory computer-readable mediumof claim 11, wherein the evasive maneuver comprises swerving, braking,or a combination thereof.
 13. The non-transitory computer-readablemedium of claim 10, wherein the instructions further cause the processorto: determine that the autonomous vehicle is in an emergency scenario inwhich the accident will occur within a threshold time period; and causethe autonomous vehicle to perform an evasive maneuver in response todetermining that the accident will occur within the threshold timeperiod.
 14. The non-transitory computer-readable medium of claim 13,wherein the evasive maneuver comprises a steering input to theautonomous vehicle to change a heading of the autonomous vehicle withoutcausing the autonomous vehicle to skid or a trailer of the autonomousvehicle to tip.
 15. The non-transitory computer-readable medium of claim13, wherein the evasive maneuver comprises a braking input to theautonomous vehicle to reduce a speed of the autonomous vehicle up to amaximum deceleration limit of the autonomous vehicle.
 16. Thenon-transitory computer-readable medium of claim 15, wherein theinstructions further cause the processor to: determine the braking inputis sufficient to prevent collision, wherein the evasive maneuvercomprises causing the autonomous vehicle to stay within a current lanein response to determining that the braking input is sufficient toprevent collision.
 17. The non-transitory computer-readable medium ofclaim 15, wherein the instructions further cause the processor to:determine that the braking is not sufficient to prevent collision,wherein the evasive maneuver comprises causing the autonomous vehicle toleave a current lane in response to determining that the braking inputis not sufficient to prevent collision.
 18. A method comprising:determining an accident in which an autonomous vehicle has been involvedbased on perception data received from at least one perception sensorconfigured to generate the perception data indicative of conditions ofthe autonomous vehicle on a roadway; determining a severity of theaccident; determining a course of action to reduce further damage to theautonomous vehicle and any other entities on or near the roadway; andcausing the autonomous vehicle to navigate based on the determinedcourse of action.
 19. The method of claim 18, further comprising:detecting a fast reveal scenario in which an occluded or obstructedentity in a current lane of the autonomous vehicle is revealed orotherwise becomes detectable by the autonomous vehicle due to a sourceof occlusion being removed based on the perception data, and cause theautonomous vehicle to perform an evasive maneuver in response todetecting the fast reveal scenario.
 20. The method of claim 18, furthercomprising: selecting and monitoring a first predicted path for each ofa first set of entities within a predetermined distance of a path oftravel of the autonomous vehicle and a second predicted path for each ofa second set of entities with a predicted path that crosses the path oftravel of the autonomous vehicle; and determine whether an escape spaceis available for the autonomous vehicle based on the first and secondpredicted paths.